Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection

Anomaly detection methods identify examples that do not follow the expected behaviour, typically in an unsupervised fashion, by assigning real-valued anomaly scores to the examples based on various heuristics. These scores need to be transformed into actual predictions by thresholding, so that the proportion of examples marked as anomalies equals the expected proportion of anomalies, called contamination factor. Unfortunately, there are no good methods for estimating the contamination factor itself. We address this need from a Bayesian perspective, introducing a method for estimating the posterior distribution of the contamination factor of a given unlabeled dataset. We leverage on outputs of several anomaly detectors as a representation that already captures the basic notion of anomalousness and estimate the contamination using a specific mixture formulation. Empirically on 22 datasets, we show that the estimated distribution is well-calibrated and that setting the threshold using the posterior mean improves the anomaly detectors' performance over several alternative methods. All code is publicly available for full reproducibility

Graph measures in task-based fMRI: Functional integration during read-out of visual and auditory information

This study investigated how attending to auditory and visual information systematically changes graph theoretical measures of integration and functional connectivity between three network modules: auditory, visual, and a joint task core. Functional MRI BOLD activity was recorded while healthy volunteers attended to colour and/or pitch information presented within an audiovisual stimulus sequence. Network nodes and modules were based on peak voxels of BOLD contrasts, including colour and pitch sensitive brain regions as well as the dorsal attention network. Network edges represented correlations between nodes' activity and were computed separately for each condition. Connection strength was increased between the task and the visual module when participants attended to colour, and between the task and the auditory module when they attended to pitch. Moreover, several nodal graph measures showed consistent changes to attentional modulation in form of stronger integration of sensory regions in response to attention. Together, these findings corroborate dynamical adjustments of both modality-specific and modality-independent functional brain networks in response to task demands and their representation in graph theoretical measures

Impaired context-sensitive adjustment of behaviour in Parkinson's disease patients tested on and off medication: An fMRI study

The brain's sensitivity to and accentuation of unpredicted over predicted sensory signals plays a fundamental role in learning. According to recent theoretical models of the predictive coding framework, dopamine is responsible for balancing the interplay between bottom-up input and top-down predictions by controlling the precision of surprise signals that guide learning. Using functional MRI, we investigated whether patients with Parkinson's disease (PD) show impaired learning from prediction errors requiring either adaptation or stabilisation of current predictions. Moreover, we were interested in whether deficits in learning over a specific time scale would be accompanied by altered surprise responses in dopamine-related brain structures. To this end, twenty-one PD patients tested on and off dopaminergic medication and twenty-one healthy controls performed a digit prediction paradigm. During the task, violations of sequence-based predictions either signalled the need to update or to stabilise the current prediction and, thus, to react to them or ignore them, respectively. To investigate contextual adaptation to prediction errors, the probability (or its inverse, surprise) of the violations fluctuated across the experiment. When the probability of prediction errors over a specific time scale increased, healthy controls but not PD patients off medication became more flexible, i.e., error rates at violations requiring a motor response decreased in controls but increased in patients. On the neural level, this learning deficit in patients was accompanied by reduced signalling in the substantia nigra and the caudate nucleus. In contrast, differences between the groups regarding the probabilistic modulation of behaviour and neural responses were much less pronounced at prediction errors requiring only stabilisation but no adaptation. Interestingly, dopaminergic medication could neither improve learning from prediction errors nor restore the physiological, neurotypical pattern. Our findings point to a pivotal role of dysfunctions of the substantia nigra and caudate nucleus in deficits in learning from flexibility-demanding prediction errors in PD. Moreover, the data witness poor effects of dopaminergic medication on learning in PD

Can we predict cognitive decline after initial diagnosis of multiple sclerosis? Results from the German National early MS cohort (KKNMS)

BackgroundCognitive impairment (CI) affects approximately one-third of the patients with early multiple sclerosis (MS) and clinically isolated syndrome (CIS). Little is known about factors predicting CI and progression after initial diagnosis.MethodsNeuropsychological screening data from baseline and 1-year follow-up of a prospective multicenter cohort study (NationMS) involving 1123 patients with newly diagnosed MS or CIS were analyzed. Employing linear multilevel models, we investigated whether demographic, clinical and conventional MRI markers at baseline were predictive for CI and longitudinal cognitive changes.ResultsAt baseline, 22% of patients had CI (impairment in 2 cognitive domains) with highest frequencies and severity in processing speed and executive functions. Demographics (fewer years of academic education, higher age, male sex), clinical (EDSS, depressive symptoms) but no conventional MRI characteristics were linked to baseline CI. At follow-up, only 14% of patients showed CI suggesting effects of retesting. Neither baseline characteristics nor initiation of treatment between baseline and follow-up was able to predict cognitive changes within the follow-up period of 1 year.ConclusionsIdentification of risk factors for short-term cognitive change in newly diagnosed MS or CIS is insufficient using only demographic, clinical and conventional MRI data. Change-sensitive, re-test reliable cognitive tests and more sophisticated predictors need to be employed in future clinical trials and cohort studies of early-stage MS to improve prediction