Biology subjects

Oedegaard, K. J.

Publications and source records attributed to Oedegaard, K. J..

2 recordsLinked to original sources

bioRxiv2020

Circadian rhythms in bipolar disorder patient-derived neurons predict lithium response

Bipolar disorder (BD) is a neuropsychiatric disorder with genetic risk factors defined by recurrent episodes of mania/hypomania, depression and circadian rhythm abnormalities. While lithium is an effective drug for BD, 30-40% of patients fail to respond adequately to treatment. Previous work has demonstrated that lithium affects the expression of "clock genes" and that lithium responders (Li-R) can be distinguished from non-responders (Li-NR) by differences in circadian rhythms. However, rhythm abnormalities in BD have not been evaluated in neurons and it is unknown if neuronal rhythms differ between Li-R and Li-NR. We used induced pluripotent stem cells (iPSCs) to culture neuronal precursor cells (NPC) and glutamatergic neurons from BD patients and controls. We identified strong circadian rhythms in Per2-luc expression in NPCs and neurons from controls and Li-R. NPC rhythms in Li-R had a shorter circadian period. Li-NR rhythms were low-amplitude and profoundly weakened. In NPCs and neurons, expression of PER2 was higher in both BD groups compared to controls. In neurons, PER2 protein expression was higher in BD than controls, especially in Li-NR samples. In single cells, NPC and neuron rhythms in both BD groups were desynchronized compared to controls. Lithium lengthened period in Li-R and control neurons but failed to alter rhythms in Li-NR. In contrast, temperature entrainment increased amplitude across all groups, and partly restored rhythms in Li-NR neurons. We conclude that neuronal circadian rhythm abnormalities are present in BD and most pronounced in Li-NR. Rhythm deficits in BD may be partly reversible through stimulation of entrainment pathways.

neuroscience↗

bioRxiv2020

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients.

Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with random oversampling class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistic. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.

bioinformatics↗