993 research outputs found
Slowing Down: Age-Related Neurobiological Predictors of Processing Speed
Processing speed, or the rate at which tasks can be performed, is a robust predictor of age-related cognitive decline and an indicator of independence among older adults. This review examines evidence for neurobiological predictors of age-related changes in processing speed, which is guided in part by our source based morphometry findings that unique patterns of frontal and cerebellar gray matter predict age-related variation in processing speed. These results, together with the extant literature on morphological predictors of age-related changes in processing speed, suggest that specific neural systems undergo declines and as a result slow processing speed. Future studies of processing speed â dependent neural systems will be important for identifying the etiologies for processing speed change and the development of interventions that mitigate gradual age-related declines in cognitive functioning and enhance healthy cognitive aging
Personality cannot be predicted from the power of resting state EEG
In the present study we asked whether it is possible to decode personality
traits from resting state EEG data. EEG was recorded from a large sample of
subjects (N = 309) who had answered questionnaires measuring personality trait
scores of the 5 dimensions as well as the 10 subordinate aspects of the Big
Five. Machine learning algorithms were used to build a classifier to predict
each personality trait from power spectra of the resting state EEG data. The
results indicate that the five dimensions as well as their subordinate aspects
could not be predicted from the resting state EEG data. Finally, to demonstrate
that this result is not due to systematic algorithmic or implementation
mistakes the same methods were used to successfully classify whether the
subject had eyes open or eyes closed and whether the subject was male or
female. These results indicate that the extraction of personality traits from
the power spectra of resting state EEG is extremely noisy, if possible at all.Comment: 14 pages, 4 figure
A functional magnetic resonance imaging study of frontal networks in obsessive-compulsive disorder during cognitive reappraisal
Background Patients with obsessive-compulsive disorder (OCD) present difficulties in the cognitive regulation of emotions, possibly because of inefficient recruitment of distributed patterns of frontal cortex regions. The aim of the present study is to characterize the brain networks, and their dysfunctions, related to emotion regulation alterations observed during cognitive reappraisal in OCD. Methods Adult patients with OCD (n = 31) and healthy controls (HC; n = 30) were compared during performance of a functional magnetic resonance imaging cognitive reappraisal protocol. We used a free independent component analysis approach to analyze network-level alterations during emotional experience and regulation. Correlations with behavioral scores were also explored. Results Analyses were focused on six networks encompassing the frontal cortex. OCD patients showed decreased activation of the frontotemporal network in comparison with HC (F(1,58) = 7.81, p = 0.007) during cognitive reappraisal. A similar trend was observed in the left frontoparietal network. Conclusions The present study demonstrates that patients with OCD show decreased activation of specific networks implicating the frontal cortex during cognitive reappraisal. These outcomes should help to better characterize the psychological processes modulating fear, anxiety, and other core symptoms of patients with OCD, as well as the associated neurobiological alterations, from a system-level perspective
Modeling Pharmacological Clock and Memory Patterns of Interval Timing in a Striatal Beat-Frequency Model with Realistic, Noisy Neurons
In most species, the capability of perceiving and using the passage of time in the seconds-to-minutes range (interval timing) is not only accurate but also scalar: errors in time estimation are linearly related to the estimated duration. The ubiquity of scalar timing extends over behavioral, lesion, and pharmacological manipulations. For example, in mammals, dopaminergic drugs induce an immediate, scalar change in the perceived time (clock pattern), whereas cholinergic drugs induce a gradual, scalar change in perceived time (memory pattern). How do these properties emerge from unreliable, noisy neurons firing in the milliseconds range? Neurobiological information relative to the brain circuits involved in interval timing provide support for an striatal beat frequency (SBF) model, in which time is coded by the coincidental activation of striatal spiny neurons by cortical neural oscillators. While biologically plausible, the impracticality of perfect oscillators, or their lack thereof, questions this mechanism in a brain with noisy neurons. We explored the computational mechanisms required for the clock and memory patterns in an SBF model with biophysically realistic and noisy MorrisâLecar neurons (SBFâML). Under the assumption that dopaminergic drugs modulate the firing frequency of cortical oscillators, and that cholinergic drugs modulate the memory representation of the criterion time, we show that our SBFâML model can reproduce the pharmacological clock and memory patterns observed in the literature. Numerical results also indicate that parameter variability (noise) â which is ubiquitous in the form of small fluctuations in the intrinsic frequencies of neural oscillators within and between trials, and in the errors in recording/retrieving stored information related to criterion time â seems to be critical for the time-scale invariance of the clock and memory patterns
Modern Views of Machine Learning for Precision Psychiatry
In light of the NIMH's Research Domain Criteria (RDoC), the advent of
functional neuroimaging, novel technologies and methods provide new
opportunities to develop precise and personalized prognosis and diagnosis of
mental disorders. Machine learning (ML) and artificial intelligence (AI)
technologies are playing an increasingly critical role in the new era of
precision psychiatry. Combining ML/AI with neuromodulation technologies can
potentially provide explainable solutions in clinical practice and effective
therapeutic treatment. Advanced wearable and mobile technologies also call for
the new role of ML/AI for digital phenotyping in mobile mental health. In this
review, we provide a comprehensive review of the ML methodologies and
applications by combining neuroimaging, neuromodulation, and advanced mobile
technologies in psychiatry practice. Additionally, we review the role of ML in
molecular phenotyping and cross-species biomarker identification in precision
psychiatry. We further discuss explainable AI (XAI) and causality testing in a
closed-human-in-the-loop manner, and highlight the ML potential in multimedia
information extraction and multimodal data fusion. Finally, we discuss
conceptual and practical challenges in precision psychiatry and highlight ML
opportunities in future research
- âŠ