Search

We introduce a new approach to monitoring the activity of smartphone users based on their physical interactions with the interface. Typical events are taps, scrolling and typing, carried out to interact with apps. As compared to other measures, this directly encapsulates potential problematic physical smartphone behaviour as a signal. The approach contrasts against conventions such as self-reporting or timing activity sessions, and it focusses on active rather than passive smartphone activity. Using this alternative method, we collected all user interface interaction events from a sample of 64 participants over a period of 8 weeks, using a bespoke monitoring app called Tymer. User Smartphone Addiction was seen to significantly correlate with high levels of interaction with Lifestyle apps, particularly for female users. Interactions with Social apps in general were also associated with Smartphone Addiction. In particular, user interactions with Snapchat correlated with Smartphone Addiction, represented across all types of interface interaction. This is significant given the widespread usage of Snapchat by teenagers, and we hypothesise that the app's design provides a particularly strong pathway in support of Smartphone Addiction.

Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain ‘truly quantitative measures’ and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with ‘standard’ and ‘state-of-the-art’ protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques., Highlights • A benchmarking database for diffusion MRI data harmonisation is presented. • The same 14 healthy controls were scanned on 3 scanners with 5 acquisition protocols. • 5 harmonisation algorithms are compared for two tasks. • 1) matched resolution scanner-to-scanner mapping, 2) spatial and angular resolution enhancement. • Cross-scanner harmonisation can reduce the variability between scanners and protocols.

Objective: The true prevalence of epileptic seizures and epilepsy in 22q11.2 deletion syndrome (22q11.2DS) is unknown, because previous studies have relied on historical medical record review. Associations of epilepsy with other neurodevelopmental manifestations (eg, specific psychiatric diagnoses) remain unexplored. Methods: The primary caregivers of 108 deletion carriers (mean age 13.6 years) and 60 control siblings (mean age 13.1 years) completed a validated epilepsy screening questionnaire. A subsample (n = 44) underwent a second assessment with interview, prolonged electroencephalography (EEG), and medical record and epileptologist review. Intelligence quotient (IQ), psychopathology, and other neurodevelopmental problems were examined using neurocognitive assessment and questionnaire/interview. Results: Eleven percent (12/108) of deletion carriers had an epilepsy diagnosis (controls 0%, P = 0.004). Fifty-seven of the remaining 96 deletion carriers (59.4%) had seizures or seizurelike symptoms (controls 13.3%, 8/60, P

We find evidence to support common behaviour in smartphone usage based on analysis of application (app) switching. This is an overlooked aspect of smartphone usage that gives additional insight beyond screen time and the particular apps that are accessed. Using a dataset of usage behaviour from 53 participants over a six-week period, we find strong similarity in the structure of networks built from app switching, despite diversity in the apps used, and the volume of app switching. App switch networks exhibit small-world, broad-scale network features, with a rapid popularity decay, suggesting that preferential attachment may drive next-app decision-making.

Schizophrenia-spectrum psychoses are highly complex and heterogeneous disorders that necessitate multiple lines of scientific inquiry and levels of explanation. In recent years, both computational and phenomenological approaches to the understanding of mental illness have received much interest, and significant progress has been made in both fields. However, there has been relatively little progress bridging investigations in these seemingly disparate fields. In this conceptual review and collaborative project from the 4th Meeting of the International Consortium on Hallucination Research, we aim to facilitate the beginning of such dialogue between fields and put forward the argument that computational psychiatry and phenomenology can in fact inform each other, rather than being viewed as isolated or even incompatible approaches. We begin with an overview of phenomenological observations on the interrelationships between auditory-verbal hallucinations (AVH) and delusional thoughts in general, before moving on to review several theoretical frameworks and empirical findings in the computational modeling of AVH. We then relate the computational models to the phenomenological accounts, with a special focus on AVH and delusions that involve the senses of agency and ownership of thought (delusions of thought interference). Finally, we offer some tentative directions for future research, emphasizing the importance of a mutual understanding between separate lines of inquiry.

There is increasing interest in exploring the use of functional MRI neurofeedback (fMRI-NF) as a therapeutic technique for a range of neurological conditions such as stroke and Parkinson's disease (PD). One main therapeutic potential of fMRI-NF is to enhance volitional control of damaged or dysfunctional neural nodes and networks via a closed-loop feedback model using mental imagery as the catalyst of self-regulation. The choice of target node/network and direction of regulation (increase or decrease activity) are central design considerations in fMRI-NF studies. Whilst it remains unclear whether the primary motor cortex (M1) can be activated during motor imagery, the supplementary motor area (SMA) has been robustly activated during motor imagery. Such differences in the regulation potential between primary and supplementary motor cortex are important because these areas can be differentially affected by a stroke or PD, and the choice of fMRI-NF target and grade of self-regulation of activity likely have substantial influence on the clinical effects and cost effectiveness of NF-based interventions. In this study we therefore investigated firstly whether healthy subjects would be able to achieve self-regulation of the hand-representation areas of M1 and the SMA using fMRI-NF training. There was a significant decrease in M1 neural activity during fMRI-NF, whereas SMA neural activity was increased, albeit not with the predicated graded effect. This study has important implications for fMRI-NF protocols that employ motor imagery to modulate activity in specific target regions of the brain and to determine how they may be tailored for neurorehabilitation.

The continuous balancing of the risks and benefits of exploiting known options or exploring new opportunities is essential to human life. We forage for new opportunities when they are deemed to be more attractive than the available option, but this decision to forage also entails costs. People differ in their propensity to exploit or forage, and both the social circumstances and our individual value orientations are likely influences. Here, participants made foraging decisions for themselves and for a charity of their choice in two paradigms: one that features two distinct modes of decision-making (foraging vs classical economic decision-making) and one which is more directly related to the classical animal foraging and ethology literature. Across both paradigms, individuals who possessed a stronger self-focused value orientation obtained more rewards when they were allowed to forage for themselves rather than the charity. Neuroimaging during the tasks revealed that this effect was associated with activity in the dorsal anterior cingulate cortex (dACC) in that more self-focused individuals showed lower activity in dACC for the self-condition relative to the other condition. This evidence reveals a dynamic interplay between foraging outcomes and the higher-order value system of individuals.

The reward-processing network is implicated in the aetiology of obesity. Several lines of evidence suggest obesity-linked genetic risk loci (such as DRD2 and FTO) may influence individual variation in body mass index (BMI) through neuropsychological processes reflected in alterations in activation of the striatum during reward processing. However, no study has tested the broader hypotheses that (a) the relationship between BMI and reward-related brain activation (measured through the blood oxygenation-dependent (BOLD) signal) may be observed in a large population study and (b) the overall genetic architecture of these phenotypes overlap, an assumption critical for the progression of imaging genetic studies in obesity research. Using data from the Human Connectome Project (N = 1055 healthy, young individuals: average BMI = 26.4), we first establish a phenotypic relationship between BMI and ventral striatal (VS) BOLD during the processing of rewarding (monetary) stimuli (β = 0.44, P = 0.013), accounting for potential confounds. BMI and VS BOLD were both significantly influenced by additive genetic factors (H2r = 0.57; 0.12, respectively). Further decomposition of this variance suggested that the relationship was driven by shared genetic (ρg = 0.47, P = 0.011), but not environmental (ρE = −0.07, P = 0.29) factors. To validate the assumption of genetic pleiotropy between BMI and VS BOLD, we further show that polygenic risk for higher BMI is also associated with increased VS BOLD response to appetitive stimuli (calorically high food images), in an independent sample (N = 81; PFWE−ROI

Introduction: The predictive processing framework has attracted much interest in the field of schizophrenia research in recent years, with an increasing number of studies also carried out in healthy individuals with nonclinical psychosis-like experiences. The current research adopted a continuum approach to psychosis and aimed to investigate different types of prediction error responses in relation to psychometrically defined schizotypy. Methods: One hundred and two healthy volunteers underwent a battery of behavioural tasks including (a) a force-matching task, (b) a Kamin blocking task, and (c) a reversal learning task together with three questionnaires measuring domains of schizotypy from different approaches. Results: Neither frequentist nor Bayesian statistical methods supported the notion that alterations in prediction error responses were related to schizotypal traits in any of the three tasks. Conclusions: These null results suggest that deficits in predictive processing associated with clinical states of psychosis are not always present in healthy individuals with schizotypal traits.

Objective: Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) uses feedback of the patient’s own brain activity to self-regulate brain networks which in turn could lead to a change in behavior and clinical symptoms. The objective was to determine the effect of NF and motor training (MOT) alone on motor and non-motor functions in Parkinson’s Disease (PD) in a 10-week small Phase I randomized controlled trial.\ud\udMethods: Thirty patients with Parkinson’s disease (PD; Hoehn and Yahr I-III) and no significant comorbidity took part in the trial with random allocation to two groups. Group 1 (NF: 15 patients) received rt-fMRI-NF with MOT. Group 2 (MOT: 15 patients) received MOT alone. The primary outcome measure was the Movement Disorder Society—Unified PD Rating Scale-Motor scale (MDS-UPDRS-MS), administered pre- and post-intervention “off-medication”. The secondary outcome measures were the “on-medication” MDS-UPDRS, the PD Questionnaire-39, and quantitative motor assessments after 4 and 10 weeks.\ud\udResults: Patients in the NF group were able to upregulate activity in the supplementary motor area (SMA) by using motor imagery. They improved by an average of 4.5 points on the MDS-UPDRS-MS in the “off-medication” state (95% confidence interval: −2.5 to −6.6), whereas the MOT group improved only by 1.9 points (95% confidence interval +3.2 to −6.8). The improvement in the intervention group meets the minimal clinically important difference which is also on par with other non-invasive therapies such as repetitive Transcranial Magnetic Stimulation (rTMS). However, the improvement did not differ significantly between the groups. No adverse events were reported in either group.\ud\udInterpretation: This Phase I study suggests that NF combined with MOT is safe and improves motor symptoms immediately after treatment, but larger trials are needed to explore its superiority over active control conditions.

Cognitive impairment is a core feature of schizophrenia but there is limited understanding of the genetic relationship between cognition in the general population and schizophrenia. We examine how common variants associated with schizophrenia en masse contribute to childhood cognitive ability in a population-based sample, and the extent to which common genetic variants associated with childhood cognition explain variation in schizophrenia. Schizophrenia polygenic risk scores were derived from the Psychiatric Genomics Consortium (n = 69 516) and tested for association with IQ, attention, processing speed, working memory, problem solving, and social cognition in over 5000 children aged 8 from the Avon Longitudinal Study of Parents and Children birth cohort. Polygenic scores for these cognitive domains were tested for association with schizophrenia in a large UK schizophrenia sample (n = 11 853). Bivariate genome-wide complex trait analysis (GCTA) estimated the amount of shared genetic factors between schizophrenia and cognitive domains. Schizophrenia polygenic risk score was associated with lower performance IQ (P = .001) and lower full IQ (P = .013). Polygenic score for performance IQ was associated with increased risk for schizophrenia (P = 3.56E-04). Bivariate GCTA revealed moderate genetic correlation between schizophrenia and both performance IQ (r G = −.379, P = 6.62E-05) and full IQ (r G = −.202, P = 5.00E-03), with approximately 14% of the genetic component of schizophrenia shared with that for performance IQ. Our results support the presence of shared common genetic factors between schizophrenia and childhood cognitive ability. We observe a genetic relationship between schizophrenia and performance IQ but not verbal IQ or other cognitive variables, which may have implications for studies utilizing cognitive endophenotypes for psychosis.