Using digital phenotyping to classify bipolar disorder and unipolar disorder - exploratory findings using machine learning models.

The aims were to investigate 1) differences in smartphone-based data on phone usage between bipolar disorder (BD) and unipolar disorder (UD) and 2) by using machine learning models, the sensitivity, specificity, and AUC of the combined smartphone data in classifying BD and UD. Daily smartphone-based self-assessments of mood and same-time passively collected smartphone data on smartphone usage was available for six months. A total of 64 patients with BD and 74 patients with UD were included.

What's Up With These Conversational Health Agents? From Users' Critiques to Implications for Design.

Recent advancements in speech recognition technology in combination with increased access to smart speaker devices are expanding conversational interactions to ever-new areas of our lives - including our health and wellbeing. Prior human-computer interaction research suggests that Conversational Agents (CAs) have the potential to support a variety of health-related outcomes, due in part to their intuitive and engaging nature. Realizing this potential requires however developing a rich understanding of users' needs and experiences in relation to these still-emerging technologies.

Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder.

Background: It is essential to differentiate bipolar disorder (BD) from unipolar disorder (UD) as the course of illness and treatment guidelines differ between the two disorders. Measurements of activity and mobility could assist in this discrimination.

Aims: 1) To investigate differences in smartphone-based location data between BD and UD, and 2) to investigate the sensitivity, specificity, and AUC of combined location data in classifying BD and UD.

Discriminating between patients with unipolar disorder, bipolar disorder, and healthy control individuals based on voice features collected from naturalistic smartphone calls.

Background: It is of crucial importance to be able to discriminate unipolar disorder (UD) from bipolar disorder (BD), as treatments, as well as course of illness, differ between the two disorders.

Aims: To investigate whether voice features from naturalistic phone calls could discriminate between (1) UD, BD, and healthy control individuals (HC); (2) different states within UD.

Methods: Voice features were collected daily during naturalistic phone calls for up to 972 days.

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states.

Background: Voice features have been suggested as objective markers of bipolar disorder (BD).

Aims: To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD.

Methods: Voice features were collected daily during naturalistic phone calls for up to 972 days.

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review.

Background: Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published.

Objective: The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies.

Multimodal cortical and hippocampal prediction of episodic-memory plasticity in young and older adults.

Episodic memory can be trained in both early and late adulthood, but there is considerable variation in cognitive improvement across individuals. Which brain characteristics make some individuals benefit more than others? We used a multimodal approach to investigate whether volumetric magnetic resonance imaging (MRI) and resting-state functional MRI characteristics of the cortex and hippocampus, brain regions involved in episodic-memory function, were predictive of cognitive improvement after memory training. We hypothesized that these brain characteristics would differentially predict memory improvement in young and older adults, given the vulnerability of cortical regions as well as the hippocampus to healthy aging.

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study.

Background: Behavioral activation is a pen and paper-based therapy form for treating depression. The patient registers their activity hourly, and together with the therapist, they agree on a plan to change behavior. However, with the limited clinical personnel, and a growing patient population, new methods are needed to advance behavioral activation.

The Temporal Dynamics of Brain Plasticity in Aging.

Cognitive training has been suggested as a possible remediation of decline in brain structure with older age. However, it is unknown whether training effects are transient or enduring, as no studies have examined training-induced plasticity relative to decline in older adults across extended periods with multiple intervention phases. We investigated the temporal dynamics of brain plasticity across periods on and off memory training, hypothesizing that (1) a decline in white matter (WM) microstructure would be observed across the duration of the study and (2) that periods of memory training would moderate the WM microstructural decline.

The corpus callosum as anatomical marker of intelligence? A critical examination in a large-scale developmental study.

Intellectual abilities are supported by a large-scale fronto-parietal brain network distributed across both cerebral hemispheres. This bihemispheric network suggests a functional relevance of inter-hemispheric coordination, a notion which is supported by a series of recent structural magnetic resonance imaging (MRI) studies demonstrating correlations between intelligence scores (IQ) and corpus-callosum anatomy. However, these studies also reveal an age-related dissociation: mostly positive associations are reported in adult samples, while negative associations are found in developing samples.

The effects of memory training on behavioral and microstructural plasticity in young and older adults.

Age differences in human brain plasticity are assumed, but have not been systematically investigated. In this longitudinal study, we investigated changes in white matter (WM) microstructure in response to memory training relative to passive and active control conditions in 183 young and older adults. We hypothesized that (i) only the training group would show improved memory performance and microstructural alterations, (ii) the young adults would show larger memory improvement and a higher degree of microstructural alterations as compared to the older adults, and (iii) changes in memory performance would relate to microstructural alterations.

White matter integrity as a marker for cognitive plasticity in aging.

Age-related differences in white matter (WM) integrity are substantial, but it is unknown whether between-subject variability in WM integrity influences the capacity for cognitive improvement. We investigated the effects of memory training related to active and passive control conditions in older adults and tested whether WM integrity at baseline was predictive of training benefits. We hypothesized that (1) memory improvement would be restricted to the training group, (2) widespread areas would show greater mean diffusivity (MD) and lower fractional anisotropy in older adults relative to young adults, and (3) within these areas, variability in WM microstructure in the older group would be predictive of training gains.

Neurodevelopmental origins of lifespan changes in brain and cognition.

Neurodevelopmental origins of functional variation in older age are increasingly being acknowledged, but identification of how early factors impact human brain and cognition throughout life has remained challenging. Much focus has been on age-specific mechanisms affecting neural foundations of cognition and their change. In contrast to this approach, we tested whether cerebral correlates of general cognitive ability (GCA) in development could be extended to the rest of the lifespan, and whether early factors traceable to prenatal stages, such as birth weight and parental education, may exert continuous influences.

Selective increase in posterior corpus callosum thickness between the age of 4 and 11years.

Establishing an efficient functional and structural connectivity between the two cerebral hemispheres is an important developmental task during childhood, and alterations in this development have accordingly been linked to a series of neurodevelopmental and pediatric disorders. The corpus callosum, the major white-matter structure connecting the hemispheres, has been shown to increase in size throughout the three first decades of life. However, behavioral studies indicate that adult-like performance levels of functional hemispheric interaction are already reached during middle and late childhood.

Accelerated longitudinal gray/white matter contrast decline in aging in lightly myelinated cortical regions.

Highly myelinated cortical regions seem to develop early and are more robust to age-related decline. By use of different magnetic resonance imaging (MRI) measures such as contrast between T1- and T2-weighted MRI scans (T1w/T2w) it is now possible to assess correlates of myelin content in vivo. Further, previous studies indicate that gray/white matter contrast (GWC) become blurred as individuals' age, apparently reflecting age-related changes in myelin structure.

Brain-computer interface using P300 and virtual reality: a gaming approach for treating ADHD.

This paper presents a novel brain-computer interface (BCI) system aiming at the rehabilitation of attention-deficit/hyperactive disorder in children. It uses the P300 potential in a series of feedback games to improve the subjects' attention. We applied a support vector machine (SVM) using temporal and template-based features to detect these P300 responses.

BCI using imaginary movements: the simulator.

Over the past two decades, much progress has been made in the rapidly evolving field of Brain Computer Interface (BCI). This paper presents a novel concept: a BCI-simulator, which has been developed for the Hex-O-Spell interface, using the sensory motor rhythms (SMR) paradigm. With the simulator, it is possible to evaluate how the model parameters such as error classifications, delay between classifications and success rate affect the communication rate.