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Gao Z, Duberg K, Warren SL, Zheng L, Hinshaw SP, Menon V, Cai W. Reduced temporal and spatial stability of neural activity patterns predict cognitive control deficits in children with ADHD. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596493. [PMID: 38854066 PMCID: PMC11160739 DOI: 10.1101/2024.05.29.596493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
This study explores the neural underpinnings of cognitive control deficits in ADHD, focusing on overlooked aspects of trial-level variability of neural coding. We employed a novel computational approach to neural decoding on a single-trial basis alongside a cued stop-signal task which allowed us to distinctly probe both proactive and reactive cognitive control. Typically developing (TD) children exhibited stable neural response patterns for efficient proactive and reactive dual control mechanisms. However, neural coding was compromised in children with ADHD. Children with ADHD showed increased temporal variability and diminished spatial stability in neural responses in salience and frontal-parietal network regions, indicating disrupted neural coding during both proactive and reactive control. Moreover, this variability correlated with fluctuating task performance and with more severe symptoms of ADHD. These findings underscore the significance of modeling single-trial variability and representational similarity in understanding distinct components of cognitive control in ADHD, highlighting new perspectives on neurocognitive dysfunction in psychiatric disorders.
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Affiliation(s)
- Zhiyao Gao
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Katherine Duberg
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Stacie L Warren
- Department of Psychology, University of Texas, Dallas, TX, USA
| | - Li Zheng
- Department of Psychology, University of Arizona, Tucson, AZ, USA
| | - Stephen P. Hinshaw
- Department of Psychology, University of California, Berkeley
- Department of Psychiatry & Behavioral Sciences, University of California, San Francisco
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Maternal & Child Health Research Institute, Stanford, CA, USA
| | - Weidong Cai
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA
- Maternal & Child Health Research Institute, Stanford, CA, USA
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Liu YF, Wilson C, Bedny M. Contribution of the language network to the comprehension of Python programming code. BRAIN AND LANGUAGE 2024; 251:105392. [PMID: 38387220 DOI: 10.1016/j.bandl.2024.105392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024]
Abstract
Does the perisylvian language network contribute to comprehension of programming languages, like Python? Univariate neuroimaging studies find high responses to code in fronto-parietal executive areas but not in fronto-temporal language areas, suggesting the language network does little. We used multivariate-pattern-analysis to test whether the language network encodes Python functions. Python programmers read functions while undergoing fMRI. A linear SVM decoded for-loops from if-conditionals based on activity in lateral temporal (LT) language cortex. In searchlight analysis, decoding accuracy was higher in LT language cortex than anywhere else. Follow up analysis showed that decoding was not driven by presence of different words across functions, "for" vs "if," but by compositional program properties. Finally, univariate responses to code peaked earlier in LT language-cortex than in the fronto-parietal network. We propose that the language system forms initial "surface meaning" representations of programs, which input to the reasoning network for processing of algorithms.
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Affiliation(s)
- Yun-Fei Liu
- Department of Psychological and Brain Sciences, Johns Hopkins Universtiy, 232 Ames Hall, 3400 N. Charles Street, Baltimore, MD 21218, USA.
| | - Colin Wilson
- Department of Cognitive Science, Johns Hopkins University, 237 Krieger Hall, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Marina Bedny
- Department of Psychological and Brain Sciences, Johns Hopkins Universtiy, 232 Ames Hall, 3400 N. Charles Street, Baltimore, MD 21218, USA
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