Evidence map›Paper›PMID 37229140›Full record

ArticleNeurobiology of language (Cambridge, Mass.)2023

Lexicality-Modulated Influence of Auditory Cortex on Subthalamic Nucleus During Motor Planning for Speech.

Alexander R Weiss, Anna Korzeniewska, Anna Chrabaszcz, Alan Bush, Julie A Fiez, Nathan E Crone, Robert M Richardson

Open access · goldAbstract read
In one paragraph

Article in Neurobiology of language (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Subthalamic nucleus-language network connectivity predicts dopaminergic modulation of speech function in Parkinson's disease.Proceedings of the National Academy of Sciences of the United States of America · 2024
    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

7 authors at 3 institutions in 1 country.

Alexander R WeissJHU Cognitive Neurophysiology and BMI Lab, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID https://orcid.org/0000-0003-2416-2883
Anna KorzeniewskaJHU Cognitive Neurophysiology and BMI Lab, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID https://orcid.org/0000-0002-9453-7751
Anna ChrabaszczDepartment of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0002-1766-7245
Alan BushBrain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.ORCID https://orcid.org/0000-0002-0407-5750
Julie A FiezDepartment of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0003-1090-2481
Nathan E CroneJHU Cognitive Neurophysiology and BMI Lab, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID https://orcid.org/0000-0001-7950-2617
Robert M RichardsonBrain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.ORCID https://orcid.org/0000-0003-2620-7387
Johns Hopkins University · USHarvard University · USUniversity of Pittsburgh · US

Funding

Cortical-Basal Ganglia Speech NetworksU01NS117836 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI RICHARDSON, ROBERT MARK · 2020 to 2024
$5.8M
Subthalamic and corticosubthalamic coding of speech productionU01NS098969 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI RICHARDSON, ROBERT MARK · 2016 to 2018
$3.3M
Temporal-spatial mapping of cortical networks important for human cognitionR01NS091139 · NINDS · JOHNS HOPKINS UNIVERSITY · PI CRONE, NATHAN E · 2015 to 2019
$1.8M
NINDS NIH HHS R01 NS091139NINDS NIH HHS U01 NS098969NINDS NIH HHS U01 NS117836
6 · The paper itself

Abstract

Speech requires successful information transfer within cortical-basal ganglia loop circuits to produce the desired acoustic output. For this reason, up to 90% of Parkinson's disease patients experience impairments of speech articulation. Deep brain stimulation (DBS) is highly effective in controlling the symptoms of Parkinson's disease, sometimes alongside speech improvement, but subthalamic nucleus (STN) DBS can also lead to decreases in semantic and phonological fluency. This paradox demands better understanding of the interactions between the cortical speech network and the STN, which can be investigated with intracranial EEG recordings collected during DBS implantation surgery. We analyzed the propagation of high-gamma activity between STN, superior temporal gyrus (STG), and ventral sensorimotor cortices during reading aloud via event-related causality, a method that estimates strengths and directionalities of neural activity propagation. We employed a newly developed bivariate smoothing model based on a two-dimensional moving average, which is optimal for reducing random noise while retaining a sharp step response, to ensure precise embedding of statistical significance in the time-frequency space. Sustained and reciprocal neural interactions between STN and ventral sensorimotor cortex were observed. Moreover, high-gamma activity propagated from the STG to the STN prior to speech onset. The strength of this influence was affected by the lexical status of the utterance, with increased activity propagation during word versus pseudoword reading. These unique data suggest a potential role for the STN in the feedforward control of speech.

Indexed as

basal gangliaelectrophysiologyevent related causalityhuman EEGlarge-scale network interactionssubthalamic nucleus

Identifiers

PMID37229140
PMCPMC10205077
OpenAlexW4307441595

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.