Pain can't be carved at the joints: defining function-based pain profiles and their relevance to chronic disease management in healthcare delivery design.

Background: Pain is a complex problem that is triaged, diagnosed, treated, and billed based on which body part is painful, almost without exception. While the "body part framework" guides the organization and treatment of individual patients' pain conditions, it remains unclear how to best conceptualize, study, and treat pain conditions at the population level. Here, we investigate (1) how the body part framework agrees with population-level, biologically derived pain profiles; (2) how do data-derived pain profiles interface with other symptom domains from a whole-body perspective; and (3) whether biologically derived pain profiles capture clinically salient differences in medical history.

Moderation Effects of Daily Behavior on Associations Between Symptoms and Social Participation Outcomes After Burn Injury: A 6-Month Digital Phenotyping Study.

Objective: To examine the moderation effects of daily behavior on the associations between symptoms and social participation outcomes after burn injury.

Design: A 6-month prospective cohort study.

Setting: Community.

The Pain Intervention & Digital Research Program: an operational report on combining digital research with outpatient chronic disease management.

Chronic pain affects up to 28% of U.S. adults, costing ∼$560 billion each year.

Neural circuit disruptions of eye gaze processing in autism spectrum disorder and schizophrenia: An activation likelihood estimation meta-analysis.

Background: Impairment in social cognition, particularly eye gaze processing, is a shared feature common to autism spectrum disorder (ASD) and schizophrenia. However, it is unclear if a convergent neural mechanism also underlies gaze dysfunction in these conditions. The present study examined whether this shared eye gaze phenotype is reflected in a profile of convergent neurobiological dysfunction in ASD and schizophrenia.

Ketamine Effects on Energy Metabolism, Functional Connectivity and Working Memory in Healthy Humans.

Working memory (WM) is a crucial resource for temporary memory storage and the guiding of ongoing behavior. N-methyl-D-aspartate glutamate receptors (NMDARs) are thought to support the neural underpinnings of WM. Ketamine is an NMDAR antagonist that has cognitive and behavioral effects at subanesthetic doses.

Systemic inflammation enhances stimulant-induced striatal dopamine elevation in tobacco smokers.

Immune-brain interactions influence the pathophysiology of addiction. Lipopolysaccharide (LPS)-induced systemic inflammation produces effects on reward-related brain regions and the dopamine system. We previously showed that LPS amplifies dopamine elevation induced by methylphenidate (MP), compared to placebo (PBO), in eight healthy controls.

Brain-phenotype models fail for individuals who defy sample stereotypes.

Individual differences in brain functional organization track a range of traits, symptoms and behaviours. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships.

The doors of precision: Reenergizing psychiatric drug development with psychedelics and open access computational tools.

Psychedelics paired with new applications of computational tools might help bypass the imprecision of psychiatric diagnosis and connect measures of behavior to specific physiologic targets.

Feasibility Analysis of Phenotype Quantification from Unstructured Clinical Interactions.

We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol.

Decision Models and Technology Can Help Psychiatry Develop Biomarkers.

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation.

Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment.

The clinical interview is the psychiatrist's data gathering procedure. However, the clinical interview is not a defined entity in the way that 'vitals' are defined as measurements of blood pressure, heart rate, respiration rate, temperature, and oxygen saturation. There are as many ways to approach a clinical interview as there are psychiatrists; and trainees can learn as many ways of performing and formulating the clinical interview as there are instructors (Nestler, 1990).

Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders.

Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory.

A hitchhiker's guide to working with large, open-source neuroimaging datasets.

Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users.

Loss of nucleus accumbens low-frequency fluctuations is a signature of chronic pain.

Chronic pain is a highly prevalent disease with poorly understood pathophysiology. In particular, the brain mechanisms mediating the transition from acute to chronic pain remain largely unknown. Here, we identify a subcortical signature of back pain.

Forced conceptual thought induced by electrical stimulation of the left prefrontal gyrus involves widespread neural networks.

Background: Early accounts of forced thought were reported at the onset of a focal seizure, and characterized as vague, repetitive, and involuntary intellectual auras distinct from perceptual or psychic hallucinations or illusions. Here, we examine the neural underpinnings involved in conceptual thought by presenting a series of 3 patients with epilepsy reporting intrusive thoughts during electrical stimulation of the left lateral prefrontal cortex (PFC) during invasive surgical evaluation. We illustrate the widespread networks involved through two independent brain imaging modalities: resting state functional magnetic resonance imaging (fMRI) (rs-fMRI) and task-based meta-analytic connectivity modeling (MACM).

Individualized functional networks reconfigure with cognitive state.

There is extensive evidence that functional organization of the human brain varies dynamically as the brain switches between task demands, or cognitive states. This functional organization also varies across subjects, even when engaged in similar tasks. To date, the functional network organization of the brain has been considered static.

Ten simple rules for predictive modeling of individual differences in neuroimaging.

Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community.

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.

Background: Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n = 306) exploring the effect of antidepressant administration on emotional face processing.

BrainMap VBM: An environment for structural meta-analysis.

The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM.

Inverse regulation of two classic Hippo pathway target genes in Drosophila by the dimerization hub protein Ctp.

The LC8 family of small ~8 kD proteins are highly conserved and interact with multiple protein partners in eukaryotic cells. LC8-binding modulates target protein activity, often through induced dimerization via LC8:LC8 homodimers. Although many LC8-interactors have roles in signaling cascades, LC8's role in developing epithelia is poorly understood.

Neurofunctional topography of the human hippocampus.

Much of what was assumed about the functional topography of the hippocampus was derived from a single case study over half a century ago. Given advances in the imaging sciences, a new era of discovery is underway, with potential to transform the understanding of healthy processing as well as the ability to treat disorders. Coactivation-based parcellation, a meta-analytic approach, and ultra-high field, high-resolution functional and structural neuroimaging to characterize the neurofunctional topography of the hippocampus was employed.

Patterns of gray matter atrophy in atypical parkinsonism syndromes: a VBM meta-analysis.

Background And Purpose: Accurate diagnosis of Atypical Parkinsonian Syndromes (APS) is important due to differences in prognosis and management, but remains a challenge in the clinical setting. The purpose of our meta-analysis was to identify characteristic patterns of gray matter atrophy in Corticobasal Degeneration (CBD), Progressive Supranuclear Palsy (PSP), Multisystem-Atrophy Parkinsonian type (MSA-P), and Idiopathic Parkinson's Disease (IPD).

Materials And Methods: Whole-brain meta-analysis was performed on 39 published voxel-based morphometry (VBM) articles (consisting of 404 IPD, 87 MSA-P, 165 CBD, and 176 PSP subjects) using the modified Anatomic Likelihood Estimation method.