High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer’s Disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer's disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN

Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease

Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest\u2014crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier\u2014FIX\u2014cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment

High-dimensional ICA analysis detects wthin-network functional connectivity damage of default-mode and sensory-motor networks in Alzheimer's disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer's disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN

Gastrointestinal stromal tumors (GISTs) and second malignancies A novel sentinel tumor? A monoinstitutional, STROBE-compliant observational analysis

Several evidences showed that patients with gastrointestinal stromal tumors (GISTs) develop additional malignancies. However, thorough incidence of second tumors remains uncertain as the possibility of a common molecular pathogenesis. A retrospective series of 128 patients with histologically proven GIST treated at our institution was evaluated. Molecular analysis of KIT and PDGFR-a genes was performed in all patients. Following the involvement of KRAS mutation in many tumors' pathogenesis, analysis of KRAS was performed in patients with also second neoplasms. Forty-six out of 128 GIST patients (35.9%) had a second neoplasm. Most second tumors (52%) raised from gastrointestinal tract and 19.6% from genitourinary tract. Benign neoplasms were also included (21.7%). Molecular analysis was available for 29/46 patients with a second tumor: wild-type GISTs (n. 5), exon 11 (n. 16), exon 13 (n. 1), exon 9 (n. 1) KIT mutations, exon 14 PDGFR-a mutation (n. 2) and exon 18 PDGFR-a mutation (n. 4). KIT exon 11 mutations were more frequent between patients who developed a second tumor (P=0.0003). Mutational analysis of KRAS showed a wild-type sequence in all cases. In metachronous cases, the median time interval between GIST and second tumor was 21.5 months. The high frequency of second tumors suggests that an unknown common molecular mechanism might play a role, but it is not likely that KRAS is involved in this common pathogenesis. The short interval between GIST diagnosis and the onset of second neoplasms asks for a careful follow-up, particularly in the first 3 years after diagnosis

100% RAG: Syracuse School of Architecture, Student Newspaper, 1989

100% RAG: Syracuse School of Architecture, Student Newspaper, 1989. Student newsletter from student contributors of Syracuse School of Architecture in 1989