Blood-Brain Barrier Disruption (BBBD)-Based Immunochemotherapy for Primary Central Nervous System Lymphoma (PCNSL), Early Results of a Phase II Study.

Primary central nervous system lymphoma is a rare but aggressive brain malignancy. It is associated with poor prognosis even with the current standard of care. The aim of this study was to evaluate the effect and tolerability of blood-brain barrier disruption treatment combined with high-dose treatment with autologous stem cell transplantation as consolidation on primary central nervous system lymphoma patients. We performed a prospective phase II study for 25 patients with previously untreated primary central nervous system lymphoma. The blood-brain barrier disruption treatment was initiated 3-4 weeks after the MATRix regimen using the previously optimized therapy protocol. Briefly, each chemotherapy cycle included two subsequent intra-arterial blood-brain barrier disruption treatments on days 1 and 2 via either one of the internal carotid arteries or vertebral arteries. Patients received the therapy in 3-week intervals. The treatment was continued for two more courses after achieving a maximal radiological response to the maximum of six courses. The complete treatment response was observed in 88.0% of the patients. At the median follow-up time of 30 months, median progression-free and overall survivals were not reached. The 2-year overall and progression-free survival rates were 67.1% and 70.3%, respectively. Blood-brain barrier disruption treatment is a promising option for primary central nervous system lymphoma with an acceptable toxicity profile.

Aberrant Functional Connectivity in the Default Mode and Central Executive Networks in Subjects with Schizophrenia - A Whole-Brain Resting-State ICA Study.

Neurophysiological changes of schizophrenia are currently linked to disturbances in connectivity between functional brain networks. Functional magnetic resonance imaging studies on schizophrenia have focused on a few selected networks. Also previously, it has not been possible to discern whether the functional alterations in schizophrenia originate from spatial shifting or amplitude alterations of functional connectivity. In this study, we aim to discern the differences in schizophrenia patients with respect to spatial shifting vs. signal amplitude changes in functional connectivity in the whole-brain connectome. We used high model order-independent component analysis to study some 40 resting-state networks (RSN) covering the whole cortex. Group differences were analyzed with dual regression coupled with y-concat correction for multiple comparisons. We investigated the RSNs with and without variance normalization in order to discern spatial shifting from signal amplitude changes in 43 schizophrenia patients and matched controls from the Northern Finland 1966 Birth Cohort. Voxel-level correction for multiple comparisons revealed 18 RSNs with altered functional connectivity, 6 of which had both spatial and signal amplitude changes. After adding the multiple comparison, y-concat correction to the analysis for including the 40 RSNs as well, we found that four RSNs showed still changes. These robust changes actually seem encompass parcellations of the default mode network and central executive networks. These networks both have spatially shifted connectivity and abnormal signal amplitudes. Interestingly the networks seem to mix their functional representations in areas like left caudate nucleus and dorsolateral prefrontal cortex. These changes overlapped with areas that have been related to dopaminergic alterations in patients with schizophrenia compared to controls.

Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity.

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.

Age-Related Differences in Functional Nodes of the Brain Cortex - A High Model Order Group ICA Study.

Functional MRI measured with blood oxygen dependent (BOLD) contrast in the absence of intermittent tasks reflects spontaneous activity of so-called resting state networks (RSN) of the brain. Group level independent component analysis (ICA) of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age-related effects from primary motor and sensory, and, higher level control RSNs. One hundred sixty-eight healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 years), 59 young adults (YA, 22.2 ± 0.6 years), and 54 older adults (OA, 42.7 ± 0.5 years), all with normal IQ. High model order group probabilistic ICA components (70) were calculated and dual-regression analysis was used to compare 21 RSN's spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN's reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs.