Biology subjects

Finke, C.

Publications and source records attributed to Finke, C..

21 records · Page 2Linked to original sources

bioRxiv2020

Vascular cognitive impairment in the mouse reshapes visual, spatial network functional connectivity.

Connectome analysis of neuroimaging data is a rapidly expanding field to identify disease specific biomarkers. Structural diffusion MRI connectivity has been useful in individuals with radiological features of small vessel disease, such as white matter hyperintensities. Global efficiency, a network metric calculated from the structural connectome, is an excellent predictor of cognitive decline. To dissect the biological underpinning of these changes, animal models are required. We tested whether the structural connectome is altered in a mouse model of vascular cognitive impairment. White matter damage was more pronounced by 6 compared to 3 months. Global efficiency remained intact, but the visual association cortex exhibited increased structural connectivity with other brain regions. Exploratory resting state functional MRI connectivity analysis revealed diminished default mode network activity in the model compared to shams. Further perturbations were observed in a primarily cortical hub and the retrosplenial and visual cortices, and the hippocampus were the most affected nodes. Behavioural deficits were observed in the cued water maze, supporting the suggestion that the visual and spatial memory networks are affected. We demonstrate specific circuitry is rendered vulnerable to vascular stress in the mouse, and the model will be useful to examine pathophysiological mechanisms of small vessel disease. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

neuroscience↗

bioRxiv2020

No impact of functional connectivity of the motor system on the resting motor threshold: A replication study.

The physiological mechanisms of corticospinal excitability and factors influencing its measurement with transcranial magnetic stimulation are still poorly understood. A recent study reported an impact of functional connectivity between the primary motor cortex and dorsal premotor cortex on the resting motor threshold of the dominant hemisphere. We aimed to replicate these findings in a larger sample of 38 healthy right-handed subjects with data from both hemispheres. Resting-state functional connectivity was assessed between the primary motor cortex and five a-priori defined motor-relevant regions on each hemisphere as well as interhemispherically between both primary motor cortices. Following the procedure by the original authors, we included age, the cortical grey matter volume and coil to cortex distance as further predictors in the analysis. We report replication models for the dominant hemisphere as well as an extension to data from both hemispheres and support the results with Bayes factors. Functional connectivity between the primary motor cortex and dorsal premotor cortex did not explain variability in the resting motor threshold and we obtained moderate evidence for the absence of this effect. In contrast, coil to cortex distance could be confirmed as an important predictor with strong evidence. These findings contradict the previously proposed effect, thus questioning the notion of the dorsal premotor cortex playing a major role in modifying corticospinal excitability.

neuroscience↗

bioRxiv2020

State-dependent signatures of Anti-NMDA-Receptor Encephalitis: a dynamic functional connectivity study

ObjectiveTraditional static functional connectivity (FC) analyses have shown functional network alterations in patients with anti-NMDA receptor encephalitis (NMDARE). Here, we use a dynamic FC approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in NMDARE and assess the discriminatory power of functional brain states in a supervised classification approach. MethodsWe included resting-state fMRI data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in FC, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. ResultsCompared to controls, patients exhibited diverging dynamic FC patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic FC measures were associated with disease severity, disease duration and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic FC models and outperformed static analyses, reaching up to 78.6% classification accuracy. ConclusionsBy applying time-resolved analyses, we disentangle state-specific FC impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on functional reorganization underlying NMDARE. Correlation of dynamic FC measures with disease symptoms and severity indicates their clinical relevance and potential as prognostic biomarkers in NMDARE.

neuroscience↗