Biology subjects

von Schwanenflug, N.

Publications and source records attributed to von Schwanenflug, N..

6 recordsLinked to original sources

bioRxiv2025

Functional connectivity is linked to symbolic BOLD patterns: replication, extension, and clinical application of the human 'complexome'

Functional connectivity (FC) quantifies the temporal coherence of blood-oxygen-level-dependent (BOLD) signals across brain regions. Recently, the information-theoretic "complexome" framework has linked FC to coinciding complexity drops: transient moments in which regional BOLD signals simultaneously become regular. Here, we replicate this relationship in an independent dataset and extend the framework by (i) integrating it with signal cofluctuation analysis through edge-timeseries, (ii) extending the previous binary concept of simultaneous complexity drops to a continuous, threshold-free calculation, (iii) providing evidence of clinical relevance in the model disease of anti-N-methyl-D-aspartate-receptor encephalitis, and (iv) deriving a novel measure of pairwise dissimilarity in local BOLD patterns. This index of pattern incongruency (IPI) explains clinically relevant FC reductions and maps onto novel associations with cognition. These findings show that global FC is closely related to local patterns within underlying BOLD signals, strengthening the link between complexity dynamics and the brains functional organization as a large-scale network.

neuroscience↗

bioRxiv2023

The formation of brain shape in human newborns

The neonatal period represents a critical phase of human brain development. During this time, the brain shows a dramatic increase in size, but it remains largely unclear how the morphology of the human brain develops in early post-partum life. Here we show that human newborns undergo a rapid formation of brain shape, beyond the expected growth in brain size. Using fractal analysis of structural neuroimaging data, we show that brain shape (i) strongly reflects infant maturity beyond differences in brain size, (ii) significantly outperforms brain size in predicting infant age at scan (mean error ~4 days), (iii) detects persistent alterations in prematurely born infants that are not captured by brain size, (iv) is consistently more sensitive to genetic similarity among neonates, and (v) is superior in predicting which newborns are twin siblings, with up to 97% accuracy. These findings identify the formation of brain shape as a fundamental maturational process in human brain development.

neuroscience↗

bioRxiv2022

Functional connectivity dynamics reflect disability and multi-domain clinical impairment in patients with relapsing-remitting multiple sclerosis

BackgroundFunctional neuroimaging studies have revealed complex and heterogeneous patterns of aberrant functional connectivity (FC) in multiple sclerosis (MS), yet it remains unclear how time-resolved FC relates to variance in clinical disease severity. ObjectivesTo characterize brain activity in MS patients with time-resolved FC analysis and explore the relationship between disease severity, multi-domain impairments, and altered network dynamics. MethodsResting-state functional MRI data were acquired from 101 MS patients and 101 age- and sex-matched healthy controls (HC). Dynamic FC analysis identified five connectivity states that were compared between HC and patients with high vs. low disability. ResultsPatients with higher disease severity exhibited a more widespread spatiotemporal pattern of altered FC and spent more time in a high-connectivity, low-occurrence state compared to patients with lower disease severity and HC. Depressive symptom severity was positively related to functional dynamics on global and network scales in patients, while fatigue and motor impairment were inversely related to frontoparietal network connectivity with the basal ganglia. ConclusionsTime-resolved FC analysis uncovered alterations in network dynamics and clinical correlations that remained undetected with a static account of brain activity. Such time-varying approaches are thus crucial for disentangling the relationship between brain dynamics, disease severity, and symptoms in MS.

neuroscience↗

bioRxiv2022

Reduced resilience of functional state transitions in patients with anti-NMDA receptor encephalitis

ObjectivePatients with anti-NMDA receptor encephalitis suffer from a severe neuropsychiatric syndrome, yet most patients show no abnormalities in routine magnetic resonance imaging. In contrast, advanced neuroimaging studies have consistently identified disrupted functional connectivity in these patients, with recent work suggesting increased volatility of functional state dynamics. Here, we investigate these network dynamics through the spatiotemporal trajectory of meta-state transitions, yielding a time-resolved account of brain state exploration in anti-NMDA receptor encephalitis. MethodsResting-state functional magnetic resonance imaging data were acquired in 73 patients with NMDAR encephalitis and 73 age- and sex-matched healthy controls. Time-resolved functional connectivity was clustered into brain meta-states, giving rise to a time-resolved transition network graph with states as nodes and transitions between brain meta-states as weighted, directed edges. Network topology, robustness, and transition cost of these transition networks were compared between groups. ResultsTransition networks of patients showed significantly lower local efficiency (t = -2.54, pFDR = 0.026), lower robustness (t = -2.01, pFDR = 0.048) and higher leap size (t = 2.33, pFDR = 0.026) compared to controls. Furthermore, the ratio of within-to-between module transitions and state similarity was significantly lower in patients. Importantly, alterations of brain state transitions correlated with disease severity. InterpretationThese findings reveal systematic alterations of transition networks in patients, suggesting that anti-NMDA receptor encephalitis is characterized by reduced stability of brain state transitions and that this reduced resilience of transition networks plays a clinically relevant role in the manifestation of the disease.

neuroscience↗

bioRxiv2021

A spatiotemporal complexity architecture of human brain activity

The human brain operates in large-scale functional networks. These networks are thought to arise from neural variability, yet the principles behind this link remain unknown. Here we report a mechanism by which the brains network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous complexity drops in functional MRI signals. These episodes support the formation of functional connections between brain regions, subserve the propagation of neural activity, and reflect inter-individual differences in age and behavior. Furthermore, complexity drops define neural states that dynamically shape the coupling strength, topological structure, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a unifying complexity architecture of neural activity - a human complexome that underpins the brains functional network organization.

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↗