Evidence map›Paper›PMID 36641365›Full record

ReviewBiological psychiatry2023

Invasive Computational Psychiatry.

Ignacio Saez, Xiaosi Gu

Open access · greenAbstract readReview
In one paragraph

Review in Biological psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 9% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Lesionectomy involving rolandic and perirolandic cortex for intractable epilepsy in children: surgical strategy and motor outcome.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025
    Article
  5. Review
  6. GEM-CRAP: a fusion architecture for focal seizure detection.Journal of translational medicine · 2025
    Article
  7. Review
  8. Article
  9. Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Ignacio SaezDepartment of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York. Electronic address: ignacio.saez@mssm.edu.
Xiaosi GuDepartment of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York. Electronic address: xiaosi.gu@mssm.edu.
Icahn School of Medicine at Mount Sinai · US

Funding

Computational and electrochemical substrates of social decision-making in humansR01MH124115 · NIMH · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI GU, XIAOSI, KISHIDA, KENNETH TUCKER · 2020 to 2024
$5.1M
Invasive decoding and stimulation of altered reward computations in depressionR01MH124763 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SAEZ, IGNACIO · 2021 to 2025
$3.9M
Neurocomputational mechanisms of proactive social behavior deficits in autism spectrum disorderR01MH122611 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FOSS-FEIG, JENNIFER, GU, XIAOSI · 2020 to 2024
$3.9M
Neural, computational and behavioral characterization of dynamic social behavior in borderline and avoidant personality disorderR01MH123069 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI, KOENIGSBERG, HAROLD W · 2021 to 2025
$3.1M
Computational and Neural Modeling of Cue Reactivity in AddictionR01DA043695 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI · 2018 to 2022
$2.9M
Electrocorticography of human prefrontal cortex during value-based decision-makingK01MH108815 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI SAEZ, IGNACIO · 2016 to 2019
$742k
Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and PerseveranceR21DA049243 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI · 2019 to 2020
$466k
Delineating proactive social behaviors in dynamic and multidimensional social spaceR21MH120789 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI, SCHILLER, DANIELA · 2019 to 2020
$466k
NIDA NIH HHS R01 DA043695NIDA NIH HHS R21 DA049243NIMH NIH HHS K01 MH108815NIMH NIH HHS R01 MH122611NIMH NIH HHS R01 MH123069NIMH NIH HHS R01 MH124115NIMH NIH HHS R01 MH124763NIMH NIH HHS R21 MH120789Wellcome Trust
6 · The paper itself

Abstract

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

Indexed as

Computer SimulationNeurosciencesPsychiatryAlgorithmsDepressionElectrophysiologyHumansModels, NeurologicalNeurophysiologySkullAlgorithmic targetingComputational psychiatryDBSECoGIntracranial neurosciencesEEG

Identifiers

PMID36641365
PMCPMC10038930
OpenAlexW4303648236

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.