Biology subjects

Otte, W. M.

Publications and source records attributed to Otte, W. M..

5 recordsLinked to original sources

bioRxiv2019

Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

An improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.

neuroscience↗

bioRxiv2019

The etiological classification of the epilepsies: A brain network underpinning?

Objectivethe current epilepsy classification is primarily clinical driven and lacks a mechanistic basis. A mechanistic basis of the classification, and within the classification especially the etiology layer, may help to better understand epilepsy and the associated comorbidities. It may also be helpful in guiding epilepsy treatment. With this study we aimed to investigate if there is a modelled mechanistic underpinning for the etiological epilepsy classification by assessing the association between epilepsy etiology and brain network topology. Methodsto that aim we assessed the association between epilepsy etiology and brain network topology. We included children referred to our outpatient first seizure clinic with suspected epilepsy who had a standard interictal EEG recording. From these EEGs, functional networks were constructed based on eyes-closed resting state time-series. Networks were characterized using measures of segregation, integration, centrality, and network strength. Principal component analyses were used to assess whether patients with epilepsy of similar etiology cluster together based on their functional brain network topology. Resultsin total, 228 children with epilepsy were included. Another 402 children served as control subjects. We were not able to detect a correlation between epilepsy etiology and functional brain network topology. We also did not find a difference in brain network topology between the controls and patients with epilepsy. Conclusionsour results do not support the presence of a brain network underpinning for the etiological epilepsy classification. This may support the hypothesis that brain network abnormalities in epilepsy are a result of ongoing seizure activity rather than the epilepsy etiology itself. Further in-depth analyses of network measures and longitudinal studies are needed to confirm this hypothesis.

neuroscience↗

bioRxiv2019

Skill acquisition increases myelination and strengthens functional connectivity in the sensorimotor circuit of the adult rat

The effects of skill acquisition on whole-brain structure and functional networks have been extensively investigated in humans but have yet to be explored in rodents. Forelimb reaching training in rodents results in well-established focal functional and structural reorganization within the motor cortex (M1) and cerebellum, indicating distributed alterations in both structure and function. However, it is unclear how local alterations in structure and function relate to distributed learning-related changes across motor networks. Here we trained adult rats in skilled reaching and used multimodal whole-brain in vivo MRI to assess both structural and functional plasticity over time.\n\nWe detected increases in a myelin-related MRI metric in white matter, cortical areas, and to a lesser extent in the cerebellum, paralleled by strengthened functional connectivity between M1 and cerebellum, possibly reflecting a decrease in cerebellum inhibition over M1. Skill learning therefore leads to myelin increases in pathways that connect sensorimotor regions, and in functional connectivity increases between areas involved in motor learning, all of which correlate with performance. These findings closely mirror previous reports of network-level changes following motor learning in humans and underlines the correspondence between human and rodent brain circuits for motor learning, despite important differences in the anatomy of physiology of movement circuits between species.

neuroscience↗

bioRxiv2018

Reliable and automatic epilepsy classification with affordable, consumer-grade electroencephalography in rural sub-Saharan Africa

Epilepsy is largely under-diagnosed in low-income and middle-income countries, due to lack of medical specialists and expensive electroencephalography (EEG) hardware. In this study we investigate if low-cost consumer-grade EEG in combination with machine learning techniques can offer a reliable screening tool to improve diagnosis rates.\n\nWe acquired brain signals in people with epilepsy (N=163) and healthy controls (N=138) in two difficult-to-reach areas in rural Guinea-Bissau and Nigeria. Five minutes of fourteen channel resting-state EEG data were acquired with a portable, low-cost consumer-grade EEG recording headset. EEG channel time-series were divided in four-second artifact-free epochs and transformed into delta, theta, alpha, beta and gamma wavelet frequencies. Summary measures such as the mean, standard deviation, minimal value and maximal value of the epoch signal fluctuations were used to train a random forest classifier. Epilepsy diagnosis based on at least three months seizure calendar data was used as the gold standard diagnosis. To prevent too optimistic classification the trained model was evaluated with EEG data from subjects not used in the training. In addition, we tested a classification model trained on Nigeria data against data from people in Guinea-Bissau and vice versa. The most contributing data features in the EEG were found in the beta and theta frequencies in Guinea-Bissau and Nigeria, respectively. Within-country model performance was good with area under the receiver-operating curves of 0.85 and 0.78 ({+/-} 0.02 standard errors) in unseen data in Guinea-Bissau and Nigeria, respectively. Across-country performance was moderate (0.62 and 0.64 {+/-} 0.02).\n\nOur data suggests that a combination of low cost electroencephalography and machine learning techniques may facilitate diagnostic screening for epilepsy in the most remote areas of the world.

neuroscience↗

bioRxiv2017

Statistical power of clinical trials has increased whilst effect size remained stable: an empirical analysis of 137 032 clinical trials between 1975-2017

BackgroundBiomedical studies with low statistical power are a major concern in the scientific community and are one of the underlying reasons for the reproducibility crisis in science. If randomized clinical trials, which are considered the backbone of evidence-based medicine, also suffer from low power, this could affect medical practice.\n\nMethodsWe analysed the statistical power in 137 032 clinical trials between 1975 and 2017 extracted from meta-analyses from the Cochrane database of systematic reviews. We determined study power to detect standardized effect sizes according to Cohen, and in meta-analysis with p-value below 0.05 we based power on the meta-analysed effect size. Average power, effect size and temporal patterns were examined.\n\nResultsThe number of trials with power [≥]80% was low but increased over time: from 9% in 1975-1979 to 15% in 2010-2014. This increase was mainly due to increasing sample sizes, whilst effect sizes remained stable with a median Cohens h of 0.21 (IQR 0.12-0.36) and a median Cohens d of 0.31 (0.19-0.51). The proportion of trials with power of at least 80% to detect a standardized effect size of 0.2 (small), 0.5 (moderate) and 0.8 (large) was 7%, 48% and 81%, respectively.\n\nConclusionsThis study demonstrates that sufficient power in clinical trials is still problematic, although the situation is slowly improving. Our data encourages further efforts to increase statistical power in clinical trials to guarantee rigorous and reproducible evidence-based medicine.

epidemiology↗