References of "TSHIBANDA, Luaba"
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See detailAnti-SOX1 antibody-associated acute hemorrhagic leukoencephalitis
Lambert, Nicolas ULiege; LUTTERI, Laurence ULiege; TSHIBANDA, Luaba ULiege et al

in Journal of Neurology (2022)

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See detailImagerie par résonance magnétique de la région hypothalamohypophysaire
BONNEVILLE, Jean-François ULiege; NECHIFOR - POTORAC, Iulia ULiege; BONNEVILLE, F et al

in EMC - Endocrinologie (2020)

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See detailBrain functional network segregation and integration in patients with disorders of consciousness
Panda, Rajanikant ULiege; Annen, Jitka ULiege; Gosseries, Olivia ULiege et al

Conference (2019, June 10)

Introduction: The brain regulates information flow by balancing integration and segregation of networks to facilitate flexible cognition and behavior. However, it is unclear how this mechanism manifests ... [more ▼]

Introduction: The brain regulates information flow by balancing integration and segregation of networks to facilitate flexible cognition and behavior. However, it is unclear how this mechanism manifests during loss of consciousness [1-3]. In this study, we studied brain network segregation and integration using resting state functional magnetic resonance imaging (fMRI) data to assess brain networks in patients with disorders of consciousness. Methods: Fifty-four patients with disorders of consciousness (24 unresponsive wakefulness syndrome (UWS) (M:F=16:8; mean age= 45±13), 30 minimally conscious state (MCS) (M:F=23:7; mean age= 36±14) and 30 age- and gender-matched healthy controls underwent fMRI. The resting-state MRI data were acquired during wakefulness with eyes closed using a 3 Tesla MRI scanner. Additionally a T1-weighted, structural imaging was performed for anatomical coregistration. First, the fMRI data were pre-processed for realignment, co-registration, segmentation, normalization, head motion regressed out and 0.01-0.1Hz band pass filtered. Data were then parcellated in 256 brain regions (ROIs) using Shen functional atlas from [4]. The connectivity matrix was computed using Pearson correlation. Graph theory connectivity was carried out to measure brain network topological properties in terms of network segregation and integration by computing binarized undirected connectivity matrix. Normalized clustering coefficients were computed as measures of network segregation while normalized participation coefficients were computed as measures of network integration [3]. Through integrated nodal graph measures, individual networks (such as default mode, frontoparietal, auditory, salience, subcortical and cerebellum networks) were also computed to study which networks were predominantly affected [3]. To enable comparison of network properties across groups, we used sparsity-based threshold (S) to avoid spurious results. To prevent biases associated with a single threshold, we determined a range of sparsity (0.06 ≤ S ≤ 0.5, with an increment of 0.025), which avoids excess network fragmentation at sparser thresholds. The between group differences for global (i.e., whole brain) and individual networks were computed with unpaired t-test with FDR correction for multiple comparison [5-6]. Finally the network segregation and integration mean values were correlated with Coma Recovery Scale-Revised (CRS-R) modified score [7]. Results: Patients in UWS had decreased participation coefficients (network integration) compared to those in MCS (effect size= -0.44, p<0.0001) and controls (effect size= -0.63, p<0.0001). Patients in MCS had significant decreased participation coefficients compared to controls (effect size= -0.37, p<0.001). On the other hand, patients in UWS had significant increased clustering coefficient (network segregation) compared to those in MCS (effect size= 0.39, p= <0.001) and controls (effect size= 0.63, p<0.0001). Patients in MCS had significant increased clustering coefficients compared to controls (effect size= 0.03, p<0.01). This decreased participation coefficient and increased clustering coefficient were noted predominantly observed in the frontoparietal and subcortical networks. Conclusions: Patients with disorders of consciousness present decreased in network integration and increased in network segregation. Notably, fragmentation of network integration is observed in patients in unaware patients (UWS), which indicates impaired information flow in the brain modules, especially in the frontoparietal and subcortical networks. This introduces a potential measure to classify patients with disorders of consciousness, which could ultimately be used for clinical diagnosis. Reference: 1. Fukushima, M., (2018). Structure–function relationships during segregated and integrated network states of human brain functional connectivity. Brain Structure and Function, 223(3), 1091-1106. 2. Deco, G., (2015). Rethinking segregation and integration: contributions of whole-brain modelling. Nature Reviews Neuroscience, 16(7), 430. 3. Keerativittayayut, R., (2018). Large-scale network integration in the human brain tracks temporal uctuations in memory encoding performance. eLife, 7, e32696. 4. Finn, E. S., (2015). Functional connectome ngerprinting: identifying individuals using patterns of brain connectivity. Nature neuroscience, 18(11), 1664. 5. Holla, B., (2017). Disrupted resting brain graph measures in individuals at high risk for alcoholism. Psychiatry Research: Neuroimaging, 265, 54-64. 6. Chennu, S., (2017). Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain, 140(8), 2120-2132. 7. Demertzi, A., (2015). Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain, 138(9), 2619-2631. [less ▲]

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See detailBOLD power spectral density differentiates patients with pathological consciousness
Alcauter, Sarael; Carrière, Manon ULiege; Raimondo, Federico ULiege et al

Poster (2019, June 10)

Introduction: Functional connectivity has been successfully used to discriminate non-sedated patients with disorders of consciousness (Demertzi et al., 2015). However, on clinical demand, patients are ... [more ▼]

Introduction: Functional connectivity has been successfully used to discriminate non-sedated patients with disorders of consciousness (Demertzi et al., 2015). However, on clinical demand, patients are evaluated under sedation to restrict motion, which considerably limits the classification of patients based on functional connectivity. It has been previously shown that changes of the frequency properties of spontaneous BOLD signal are of cognitive relevance even in sleeping neonates (Alcauter et al., 2015). We therefore aimed at exploring the automatic discrimination of sedated patients in the clinical entities of minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS), based on the frequency profile of the BOLD signal. Methods: Forty-four patients with MCS (n=26) or VS/UWS (n=18), based on the Coma Recovery Scale-Revised (CRS-R), were scanned on a 3T MRI scanner. Images of the whole brain were acquired with BOLD-sensitive sequences (300 volumes, TR=2s, TE=30ms, voxel size=3x3x3 mm3) and a T1 (TR=2.3s, TE=2.47ms, voxel size = 1x1x1.2 mm3). Sedative agents (propofol, sevoflurane, or a combination of both) were administered using the minimum necessary dose. Preprocessing of functional images included slice-time correction, realignment, segmentation, normalisation, and smoothing (6mm FWHM). Noise reduction included detection and regression of motion outliers (ART toolbox), anatomical component-based correction, and regression of motion parameters, no temrporal filtering was applied. The average power density between 0.01 and 0.1 Hz (classic frequency band for resting state analyses) was estimated and divided by the total power density, for each voxel. Supervised classification of patients in MCS or UWS was explored with Support Vector Machine classifiier using stratified 5-fold cross-validation. The clusters with significant differences between groups (p<0.005, uncorrected; cluster size > 10 voxels) in the training sets were selected as features. The 5-fold validation was repeated 20 times to estimate the variability of the classification accuracies and the frequency of each voxel being selected as a relevant feature. Results:The average classification accuracy was 79%±5 (SD), with average sensitivity 76%±10, and specificity 81%±9. The most frequently selected regions as features included the superior parietal lobule (Frequency: 100%; MNI x, y, z (mm): -26, -50, 64), putamen (97%; -30, -6, -8), occipital fusiform gyrus (92%; -34, -70, -20), occipital pole (65%; 22, -98, 16), angular gyrus (54%; -60, -58, 32). Conclusions: The power spectral density of the spontaneous BOLD signal under anesthesia allowed to classify individual patients with MCS and UWS with 79% accuracy. The most frequent selected features included association areas in the parietal and occipital lobes and the putamen. Further validation with independent cohorts is needed to generalize the current findings. Taken together, the use of power spectral density may represent an alternative to functional connectivity to classify patients with consciousness disorders under anesthesia, therefore capturing properties of conscious function beyond reportability. [less ▲]

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See detailAdipsic diabetes insipidus revealing a bifocal intracranial germinoma
KREUTZ, Julie ULiege; Potorac, Iulia ULiege; LUTTERI, Laurence ULiege et al

in Annales d'Endocrinologie (2017)

Abstract Adipsic diabetes insipidus is a rare complication of intracranial tumors in which impaired antidiuretic hormone secretion is associated with the loss of thirst sensation. Here, we present the ... [more ▼]

Abstract Adipsic diabetes insipidus is a rare complication of intracranial tumors in which impaired antidiuretic hormone secretion is associated with the loss of thirst sensation. Here, we present the case of a patient with bifocal intracranial germinoma, diagnosed due to symptoms mainly caused by adipsic diabetes insipidus. This is, to our knowledge, the first case of adipsic diabetes insipidus revealing an intracranial germinoma reported in the literature. We describe the diagnostic procedures and the three-year follow-up of this patient. Management of intracranial germ-cell tumors is made complex by the wide range of histological features. Although germinomas have a generally better prognosis than most nongerminomatous tumors, they can have severe or even life-threatening presentations. Adipsic diabetes insipidus is one such severe presentation and its rarity can make it difficult to recognize and manage. Awareness of this potential entity is therefore important for clinical practice. Le diabète insipide adipsique est une des rares complications des tumeurs intracrâniennes. Il associe une baisse de la sécrétion d’hormone antidiurétique à une perte de la sensation de soif et ilsignale souvent la présence d’une lésion qui atteint ou envahit l’hypothalamus. Nous présentons le cas d’une patiente avec un germinome intracrânien bifocal diagnostiqué devant un tableau de diabète insipide adipsique. À notre connaissance, il s’agit du premier cas de la littérature d’un diabète insipide révélant un germinome intracrânien. La prise en charge des tumeurs germinales intracrâniennes est complexe du fait des phénotypes histologiques divers. Bien que les germinomes ont généralement un meilleur pronostic que les tumeurs non-germinomateuses, ils peuvent avoir des présentations sévères. Le diabète insipide adipsique est une de ces présentations sévères et sa rareté peut rendre son diagnostic et sa prise en charge difficiles. La reconnaissance de cette entité potentielle est, dès lors, importante pour la pratique clinique [less ▲]

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See detailA method for independent component graph analysis of resting-state fMRI.
Ribeiro de Paula, Demetrius; Ziegler, Erik ULiege; Abeyasinghe, P et al

in Brain and Behavior (2017), 7(3),

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See detailMultivariate functional network connectivity for disorders of consciousness
Rudas, Jorge; Martínez, Darwin; Demertzi, Athina ULiege et al

in Lecture Notes in Computer Science (2017), 10125 LNCS

Recent evidence suggests that healthy brain is organized on large-scale spatially distant brain regions, which are temporally synchronized. These regions are known as resting state networks (RSNs). The ... [more ▼]

Recent evidence suggests that healthy brain is organized on large-scale spatially distant brain regions, which are temporally synchronized. These regions are known as resting state networks (RSNs). The level of interaction among these functional entities has been studied in the so called functional network connectivity (FNC). FNC aims to quantify the level of interaction between pairs of RSNs, which commonly emerge at similar spatial scale. Nevertheless, the human brain is a complex functional structure which is partitioned into functional regions that emerge at multiple spatial scales. In this work, we propose a novel multivariate FNC strategy to study interactions among communities of RSNs, these communities may emerge at different spatial scales. For this, first a community or hyperedge detection strategy was used to conform groups of RSNs with a similar behavior. Following, a distance correlation measurement was employed to quantify the level of interaction between these communities. The proposed strategy was evaluated in the characterization of patients with disorders of consciousness, a highly challenging problem in the clinical setting. The results suggest that the proposed strategy may improve the capacity of characterization of these brain altered conditions. [less ▲]

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See detailNon-invasive approaches in the diagnosis of acute rejection in kidney transplant recipients. Part I. In vivo imaging methods.
HANSSEN, Oriane ULiege; Erpicum, Pauline ULiege; LOVINFOSSE, Pierre ULiege et al

in Clinical Kidney Journal (2017), 10(1), 97-105

Kidney transplantation (KTx) represents the best available treatment for patients with end-stage renal disease. Still, full benefits of KTx are undermined by acute rejection (AR). The diagnosis of AR ... [more ▼]

Kidney transplantation (KTx) represents the best available treatment for patients with end-stage renal disease. Still, full benefits of KTx are undermined by acute rejection (AR). The diagnosis of AR ultimately relies on transplant needle biopsy. However, such an invasive procedure is associated with a significant risk of complications and is limited by sampling error and interobserver variability. In the present review, we summarize the current literature about non-invasive approaches for the diagnosis of AR in kidney transplant recipients (KTRs), including in vivo imaging, gene expression profiling and omics analyses of blood and urine samples. Most imaging techniques, like contrast-enhanced ultrasound and magnetic resonance, exploit the fact that blood flow is significantly lowered in case of AR-induced inflammation. In addition, AR-associated recruitment of activated leukocytes may be detectable by 18F-fluoro-deoxy-glucose positron emission tomography. In parallel, urine biomarkers, including CXCL9/CXCL10 or a three-gene signature of CD3epsilon, IP-10 and 18S RNA levels, have been identified. None of these approaches has been adopted yet in the clinical follow-up of KTRs, but standardization of procedures may help assess reproducibility and compare diagnostic yields in large prospective multicentric trials. [less ▲]

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See detailNon-invasive approaches in the diagnosis of acute rejection in kidney transplant recipients, part II: omics analyses of urine and blood samples
Erpicum, Pauline ULiege; HANSSEN, Oriane ULiege; WEEKERS, Laurent ULiege et al

in NDT Plus (2017)

Kidney transplantation (KTx) represents the best available treatment for patients with end-stage renal disease. Still, the full benefits of KTx are undermined by acute rejection (AR). The diagnosis of AR ... [more ▼]

Kidney transplantation (KTx) represents the best available treatment for patients with end-stage renal disease. Still, the full benefits of KTx are undermined by acute rejection (AR). The diagnosis of AR ultimately relies on transplant needle biopsy. However, such an invasive procedure is associated with a significant risk of complications and is limited by sampling error and interobserver variability. In the present review, we summarize the current literature about non-invasive approaches for the diagnosis of AR in kidney transplant recipients (KTRs), including in vivo imaging, gene-expression profiling and omics analyses of blood and urine samples. Most imaging techniques, such as contrast-enhanced ultrasound and magnetic resonance, exploit the fact that blood flow is significantly lowered in case of AR-induced inflammation. In addition, AR-associated recruitment of activated leucocytes may be detectable by 18F-fluorodeoxyglucose positron emission tomography. In parallel, urine biomarkers, including CXCL9/CXCL10 or a three-gene signature of CD3ε, CXCL10 and 18S RNA levels, have been identified. None of these approaches has yet been adopted in the clinical follow-up of KTRs, but standardization of analysis procedures may help assess reproducibility and comparative diagnostic yield in large, prospective, multicentre trials. [less ▲]

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See detailMaladie de Cushing et kyste de la poche de Rathke : un défi diagnostique
VROONEN, Laurent ULiege; KREUTZ, Julie ULiege; Potorac, Iulia ULiege et al

in Annales d'Endocrinologie : 33ème congrès de la Société Française d'Endocrinologie (2016, October)

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See detailT2-weighted MRI signal predicts hormone and tumor responses to somatostatin analogs in acromegaly.
Potorac, Iulia ULiege; PETROSSIANS, Patrick ULiege; Daly, Adrian ULiege et al

in Endocrine-Related Cancer (2016), 23(11), 871881

GH-secreting pituitary adenomas can be hypo-, iso- or hyperintense on T2-weighted MRI sequences. We conducted the current multicenter study in a large population of patients with acromegaly to analyze the ... [more ▼]

GH-secreting pituitary adenomas can be hypo-, iso- or hyperintense on T2-weighted MRI sequences. We conducted the current multicenter study in a large population of patients with acromegaly to analyze the relationship between T2-weighted signal intensity on diagnostic MRI and hormonal and tumoral responses to somatostatin analogs (SSA) as primary monotherapy. Acromegaly patients receiving primary SSA for at least 3 months were included in the study. Hormonal, clinical and general MRI assessments were performed and assessed centrally. We included 120 patients with acromegaly. At diagnosis, 84, 17 and 19 tumors were T2-hypo-, iso- and hyperintense, respectively. SSA treatment duration, cumulative and mean monthly doses were similar in the three groups. Patients with T2-hypointense adenoma had median SSA-induced decreases in GH and IGF-1 of 88% and 59% respectively, which were significantly greater than the decreases observed in the T2-iso- and hyperintense groups (p<0.001). Tumor shrinkage on SSA was also significantly greater in the T2-hypointense group (38%) compared with the T2-iso- and hyperintense groups (8% and 3%, respectively; p<0.0001). The response to SSA correlated with the calculated T2-intensity: the lower the T2-weighted intensity, the greater the decrease of random GH (p<0.0001, r=0.22), IGF-1 (p<0.0001, r=0.14) and adenoma volume (p<0.0001, r=0.33). The T2-weighted signal intensity of GH-secreting adenomas at diagnosis correlates with the hormone reduction and tumor shrinkage in response to primary SSA treatment in acromegaly. This study supports its use as a generally available predictive tool at diagnosis that could help to guide subsequent treatment choices in acromegaly. [less ▲]

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See detailFunction–structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET
Annen, Jitka ULiege; Heine, Lizette ULiege; Ziegler, Erik ULiege et al

in Human Brain Mapping (2016), 37(11), 3707-3720

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See detailCorrelation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness
Soddu, Andrea ULiege; Gomez, Francisco; Heine, Lizette ULiege et al

in Brain and Behavior (2016), 6(1), 1-15

Introduction: The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure ‘resting state’ cerebral metabolism. This technique made ... [more ▼]

Introduction: The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure ‘resting state’ cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness. Objective: We assessed the possi- bility of creating functional MRI activity maps, which could estimate the rela- tive levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recogniz- ing individual networks of independent component selection in functional mag- netic resonance imaging (fMRI) resting state analysis. Methods: We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neu- ronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients. Results: The results show a significant similarity with q = 0.75  0.05 for healthy controls and q = 0.58  0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG- PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls. Conclusions: The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map. [less ▲]

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See detailNeural correlates of consciousness in patients who have emerged from a minimally conscious state: A cross-sectional multimodal imaging study
Di Perri, Carol ULiege; Bahri, Mohamed Ali ULiege; Amico, Enrico ULiege et al

in Lancet Neurology (2016), 15

Background Between pathologically impaired consciousness and normal consciousness exists a scarcely researched transition zone, referred to as emergence from minimally conscious state, in which patients ... [more ▼]

Background Between pathologically impaired consciousness and normal consciousness exists a scarcely researched transition zone, referred to as emergence from minimally conscious state, in which patients regain the capacity for functional communication, object use, or both. We investigated neural correlates of consciousness in these patients compared with patients with disorders of consciousness and healthy controls, by multimodal imaging. Methods In this cross-sectional, multimodal imaging study, patients with unresponsive wakefulness syndrome, patients in a minimally conscious state, and patients who had emerged from a minimally conscious state, diagnosed with the Coma Recovery Scale–Revised, were recruited from the neurology department of the Centre Hospitalier Universitaire de Liège, Belgium. Key exclusion criteria were neuroimaging examination in an acute state, sedation or anaesthesia during scanning, large focal brain damage, motion parameters of more than 3 mm in translation and 3° in rotation, and suboptimal segmentation and normalisation. We acquired resting state functional and structural MRI data and ¹⁸F-fl uorodeoxyglucose (FDG) PET data; we used seed-based functional MRI (fMRI) analysis to investigate positive default mode network connectivity (within-network correlations) and negative default mode network connectivity (between-network anticorrelations). We correlated FDG-PET brain metabolism with fMRI connectivity. We used voxel- based morphometry to test the eff ect of anatomical deformations on functional connectivity. Findings We recruited a convenience sample of 58 patients (21 [36%] with unresponsive wakefulness syndrome, 24 [41%] in a minimally conscious state, and 13 [22%] who had emerged from a minimally conscious state) and 35 healthy controls between Oct 1, 2009, and Oct 31, 2014. We detected consciousness-level-dependent increases (from unresponsive wakefulness syndrome, minimally conscious state, emergence from minimally conscious state, to healthy controls) for positive and negative default mode network connectivity, brain metabolism, and grey matter volume (p<0·05 false discovery rate corrected for multiple comparisons). Positive default mode network connectivity diff ered between patients and controls but not among patient groups (F test p<0·0001). Negative default mode network connectivity was only detected in healthy controls and in those who had emerged from a minimally conscious state; patients with unresponsive wakefulness syndrome or in a minimally conscious state showed pathological between-network positive connectivity (hyperconnectivity; F test p<0·0001). Brain metabolism correlated with positive default mode network connectivity (Spearman’s r=0·50 [95% CI 0·26 to 0·61]; p<0·0001) and negative default mode network connectivity (Spearman’s r=–0·52 [–0·35 to –0·67); p<0·0001). Grey matter volume did not diff er between the studied groups (F test p=0·06). Interpretation Partial preservation of between-network anticorrelations, which are seemingly of neuronal origin and cannot be solely explained by morphological deformations, characterise patients who have emerged from a minimally conscious state. Conversely, patients with disorders of consciousness show pathological between-network correlations. Apart from a deeper understanding of the neural correlates of consciousness, these fi ndings have clinical implications and might be particularly relevant for outcome prediction and could inspire new therapeutic options. [less ▲]

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See detailHow T2-weighted signal intensity of GH-secreting adenomas correlates wit response to primary somatostatin analogue therapy in acromegaly
Potorac, Iulia ULiege; PETROSSIANS, Patrick ULiege; Daly, Adrian ULiege et al

in Abstract book - 25th meeting of the Belgian Endocrine Society (2015, October)

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See detailUne lésion sellaire d'évolution hautement fluctuante
BETEA, Daniela ULiege; Potorac, Iulia ULiege; BONNEVILLE, Jean-François ULiege et al

in Abstract book - Annales d'Endocrinologie - 32ème Congrès de la Société Française d'Endocrinologie (2015, October)

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See detailThalamic and extrathalamic mechanisms of consciousness after severe brain injury.
Lutkenhoff, Evan; Chiang, Jeffrey; TSHIBANDA, Luaba ULiege et al

in Annals of Neurology (2015)

Abstract OBJECTIVE: What mechanisms underlie the loss and recovery of consciousness after severe brain injury? We sought to establish, in the largest cohort of patients with disorders of consciousness ... [more ▼]

Abstract OBJECTIVE: What mechanisms underlie the loss and recovery of consciousness after severe brain injury? We sought to establish, in the largest cohort of patients with disorders of consciousness (DOC) to date, the link between gold standard clinical measures of awareness and wakefulness, and specific patterns of local brain pathology-thereby possibly providing a mechanistic framework for patient diagnosis, prognosis, and treatment development. METHODS: Structural T1-weighted magnetic resonance images were collected, in a continuous sample of 143 severely brain-injured patients with DOC (and 96 volunteers), across 2 tertiary expert centers. Brain atrophy in subcortical regions (bilateral thalamus, basal ganglia, hippocampus, basal forebrain, and brainstem) was assessed across (1) healthy volunteers and patients, (2) clinical entities (eg, vegetative state, minimally conscious state), (3) clinical measures of consciousness (Coma Recovery Scale-Revised), and (4) injury etiology. RESULTS: Compared to volunteers, patients exhibited significant atrophy across all structures (p < 0.05, corrected). Strikingly, we found almost no significant differences across clinical entities. Nonetheless, the clinical measures of awareness and wakefulness upon which differential diagnosis rely were systematically associated with tissue atrophy within thalamic and basal ganglia nuclei, respectively; the basal forebrain was atrophied in proportion to patients' response to sensory stimulation. In addition, nontraumatic injuries exhibited more extensive thalamic atrophy. INTERPRETATION: These findings provide, for the first time, a grounding in pathology for gold standard behavior-based clinical measures of consciousness, and reframe our current models of DOC by stressing the different links tying thalamic mechanisms to willful behavior and extrathalamic mechanisms to behavioral (and electrocortical) arousal. Ann Neurol 2015. [less ▲]

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See detailIntrinsic functional connectivity differentiates minimally conscious from unresponsive patients
Demertzi, Athina ULiege; Antonopoulos, Georgios ULiege; Heine, Lizette ULiege et al

in Brain: a Journal of Neurology (2015), 138

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See detailAutomatic identification of resting state networks: An extended version of multiple template-matching
Guaje, Javier; Molina, Juan; Rudas, Jorge et al

in Proceedings of SPIE: The International Society for Optical Engineering (2015), 9681

Functional magnetic resonance imaging in resting state (fMRI-RS) constitutes an informative protocol to investigate several pathological and pharmacological conditions. A common approach to study this ... [more ▼]

Functional magnetic resonance imaging in resting state (fMRI-RS) constitutes an informative protocol to investigate several pathological and pharmacological conditions. A common approach to study this data source is through the analysis of changes in the so called resting state networks (RSNs). These networks correspond to well-defined functional entities that have been associated to different low and high brain order functions. RSNs may be characterized by using Independent Component Analysis (ICA). ICA provides a decomposition of the fMRI-RS signal into sources of brain activity, but it lacks of information about the nature of the signal, i.e., if the source is artifactual or not. Recently, a multiple template-matching (MTM) approach was proposed to automatically recognize RSNs in a set of Independent Components (ICs). This method provides valuable information to assess subjects at individual level. Nevertheless, it lacks of a mechanism to quantify how much certainty there is about the existence/absence of each network. This information may be important for the assessment of patients with severely damaged brains, in which RSNs may be greatly affected as a result of the pathological condition. In this work we propose a set of changes to the original MTM that improves the RSNs recognition task and also extends the functionality of the method. The key points of this improvement is a standardization strategy and a modification of method's constraints that adds flexibility to the approach. Additionally, we also introduce an analysis to the trustworthiness measurement of each RSN obtained by using template-matching approach. This analysis consists of a thresholding strategy applied over the computed Goodness-of-Fit (GOF) between the set of templates and the ICs. The proposed method was validated on 2 two independent studies (Baltimore, 23 healthy subjects and Liege, 27 healthy subjects) with different configurations of MTM. Results suggest that the method will provide complementary information for characterization of RSNs at individual level. © 2015 SPIE. [less ▲]

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