ReviewCurrent opinion in neurobiology2019
Harnessing networks and machine learning in neuropsychiatric care.
Review in Current opinion in neurobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Serotonergic cortico-limbic and executive network dysfunction in Parkinson's disease impulse control disorders: a PET-fMRI study.NPJ Parkinson's disease · 2026Article
- Identifying key brain pathology in bipolar and unipolar depression using a region-specific brain aging trajectories approach: Insights from the Taiwan Aging and Mental Illness Cohort.Psychological medicine · 2025Article
- Natural trajectory subclasses of cognitive impairment in breast cancer patients experiencing insomnia.Cancer · 2025Article
- Brain-based predictive modeling of mind-wandering and involuntary thought: the idiographic approach.Technology, mind, and behavior · 2025Article
- Functional brain connectivity changes associated with day-to-day fluctuations in affective states.Cognitive, affective & behavioral neuroscience · 2024Article
- Review
- Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment.Frontiers in medicine · 2023Review
- Cross-Sectional Characterization of Local Brain Network Connectivity Pre and Post Breast Cancer Treatment and Distinct Association With Subjective Cognitive and Psychological Function.Frontiers in neurology · 2021Article
- Functional connectome biotypes of chemotherapy-related cognitive impairment.Journal of cancer survivorship : research and practice · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The development of next-generation therapies for neuropsychiatric illness will likely rely on a precise and accurate understanding of human brain dynamics. Toward this end, researchers have focused on collecting large quantities of neuroimaging data. For simplicity, we will refer to large cross-sectional neuroimaging studies as broad studies and to intensive longitudinal studies as deep studies. Recent progress in identifying illness subtypes and predicting treatment response in neuropsychiatry has been supported by these study designs, along with methods bridging machine learning and network science. Such methods combine analytic power, interpretability, and direct connection to underlying theory in cognitive neuroscience. Ultimately, we propose a general framework for the treatment of neuropsychiatric illness relying on the findings from broad and deep studies combined with basic cognitive and physiologic measurements.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.