ICM Partners

About: ICM Partners is a based out in . It is known for research contribution in the topics: Population & Breast cancer. The organization has 1311 authors who have published 1521 publications receiving 33745 citations. The organization is also known as: International Creative Management Partners.

Juvenile Frontotemporal Dementia with Parkinsonism Associated with Tau Mutation G389R

Abstract: Frontotemporal lobe degeneration includes a large spectrum of neurodegenerative disorders. Patients with frontotemporal dementia with parkinsonism linked to chromosome 17 exhibit heterogeneity in both clinical and neuropathological features. Here, we report the case of a young patient with a G389R mutation. This teenager girl was 17 years old when she progressively developed severe behavioral disturbances. First, she was considered to be suffering from atypical depression. After 2 years, she was referred to the department of neurology. By this time, the patient exhibited typical frontotemporal dementia with mild extrapyramidal disorders. The main behavioral features included apathy and reduced speech output. MRI and SPECT showed a frontotemporal atrophy and hypofixation, respectively. She died 7 years after onset. Three relatives on her father side had also died after early onset dementia. Genetic testing revealed a heterozygous guanine to cytosine mutation at the first base of codon 389 (Exon 13) of MAPT, the tau gene, resulting in a glycine to arginine substitution, in the patient and her non-affected father. Postmortem neuropathological and biochemical data indicate a Pick-like tau pathology but with phosphoserine 262-positive immunoreactivity. This case is remarkable because of the extremely early onset of the disease.

A statistical model for brain networks inferred from large-scale electrophysiological signals.

Abstract: Network science has been extensively developed to characterize the structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graph models (ERGMs) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset of N ¼ 108 healthy subjects during eyes-open (EO) and eyes closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes than other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in real brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8–13 Hz) than EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14–29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the functional segregation and integration of the brain in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

Blood-brain barrier disruption with low-intensity pulsed ultrasound for the treatment of pediatric brain tumors: a review and perspectives

Abstract: Pediatric brain tumors are the most common solid tumor and the first cause of cancer death in childhood, adolescence, and young adulthood. Current treatments are far from optimal in most of these tumors and the prognosis remains dismal for many of them. One of the main causes of the failure of current medical treatments is in part due to the existence of the blood-brain barrier (BBB), which limits drug delivery to tumors. Opening of the BBB with low-intensity pulsed ultrasound (LIPU) has emerged during the last 2 decades as a promising technique for enhancing drug delivery to the brain. In preclinical models, enhanced delivery of a wide range of therapeutic agents, from low-molecular-weight drugs, to antibodies and immune cells, has been observed as well as tumor control and increased survival. This technique has recently entered clinical trials with extracranial and intracranial devices. The safety and feasibility of this technique has furthermore been shown in patients treated monthly for recurrent glioblastoma receiving carboplatin chemotherapy. In this review, the characteristics of the BBB in the most common pediatric brain tumors are reviewed. Then, principles and mechanisms of BBB disruption with ultrasound (US) are summarized and described at the histological and biological levels. Lastly, preclinical studies that have used US-induced BBB opening in tumor models, recent clinical trials, and the potential use of this technology in pediatrics are provided.

Longitudinal changes in functional connectivity of cortico-basal ganglia networks in manifests and premanifest huntington's disease.

Abstract: Huntington's disease (HD) is a genetic neurological disorder resulting in cognitive and motor impairments. We evaluated the longitudinal changes of functional connectivity in sensorimotor, associative and limbic cortico-basal ganglia networks. We acquired structural MRI and resting-state fMRI in three visits one year apart, in 18 adult HD patients, 24 asymptomatic mutation carriers (preHD) and 18 gender- and age-matched healthy volunteers from the TRACK-HD study. We inferred topological changes in functional connectivity between 182 regions within cortico-basal ganglia networks using graph theory measures. We found significant differences for global graph theory measures in HD but not in preHD. The average shortest path length (L) decreased, which indicated a change toward the random network topology. HD patients also demonstrated increases in degree k, reduced betweeness centrality bc and reduced clustering C. Changes predominated in the sensorimotor network for bc and C and were observed in all circuits for k. Hubs were reduced in preHD and no longer detectable in HD in the sensorimotor and associative networks. Changes in graph theory metrics (L, k, C and bc) correlated with four clinical and cognitive measures (symbol digit modalities test, Stroop, Burden and UHDRS). There were no changes in graph theory metrics across sessions, which suggests that these measures are not reliable biomarkers of longitudinal changes in HD. preHD is characterized by progressive decreasing hub organization, and these changes aggravate in HD patients with changes in local metrics. HD is characterized by progressive changes in global network interconnectivity, whose network topology becomes more random over time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.