Biology subjects

Johansen-Berg, H.

Publications and source records attributed to Johansen-Berg, H..

3 recordsLinked to original sources

bioRxiv2018

Causal explanation of individual differences in human sensorimotor memory formation

Sensorimotor cortex mediates the formation of adaptation memory. Individuals differ in the rate at which they acquire, retain, and generalize adaptation. We present a mechanistic explanation of the neurochemical and computational causes of this variation in humans. Neuroimaging identified structural, functional and neurochemical covariates of a computational parameter that determines memory persistence. To establish causality, we increased sensorimotor cortex excitability during adaptation, using transcranial direct current stimulation. As predicted, this increased retention. Inter-individual variance in the stimulation-induced E:I increase predicted the computational change, which predicted the memory gain. These relations did not hold, and memory was unchanged, with stimulation applied before adaptation. This cognitive state dependent effect was modulated by the BDNF val66met genetic polymorphism. Memory was enhanced by stimulation in Val/Val carriers only, implicating a mechanistic role for activity-dependent BDNF secretion. Sensorimotor cortex E:I causally determines the time constant of memory persistence, explaining phenotypic variation in adaptation decay.

neuroscience↗

bioRxiv2017

Automated detection of sleep-boundary times using wrist-worn accelerometry

ObjectiveCurrent polysomnography-validated measures of sleep status from wrist-worn accelerometers cannot be used in fully automated analysis as they rely on self-reported sleep-onset and -end (sleep-boundary) information. We set out to develop an automated, data-driven approach to sleep-boundary detection from wrist-worn accelerometer data.\n\nMethodsOn three separate occasions, participants were asked to wear a GENEActiv(R) wrist-worn accelerometer for nine days and concurrently complete sleep diaries with lights-off, asleep and wake-up information. We developed and evaluated three data-driven methods for sleep-boundary detection: a change-point detection based method, a thresholding method and a random forest classifier based method. Mean absolute errors between automatically-derived and self-reported sleep-onset and wake-up times were recorded in addition to kappa statistics for the minute-by-minute performance of each of the methods.\n\nResults46 participants provided 972 days of accelerometer recordings with corresponding self-reported sleep information. The three sleep-boundary detection methods resulted in mean absolute errors in sleep-onset and wake-up times per individual of 36 min, 34 min and 33 min and kappa statistics of 0.87, 0.89 and 0.89, respectively.\n\nConclusionOur methods provide a data-driven approach to detect sleep-onset and -end times without the need for self-reported sleep-boundary information. The methods are likely to be of particular use for large-scale studies where the collection of self-reported sleep diaries is impractical.\n\nSignificanceObjective measures of sleep are needed to reliably detect associations with health outcomes. This work lays the foundation for studies of objectively measured sleep duration and its health consequences in large studies.

epidemiology↗

bioRxiv2017

The Role Of Diffusion MRI In Neuroscience

Diffusion weighted imaging has further pushed the boundaries of neuroscience by allowing us to peer farther into the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We will briefly summarise key questions, that have historically been raised in neuroscience, concerning the brains white matter. We will then expand on the benefits of diffusion weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review will highlight the invaluable contribution of diffusion weighted imaging in neuroscience, present its limitations and put forth new challenges for the future generations who may wish to exploit this powerful technology to gain novel insights.

neuroscience↗