Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease

Ds, Barron; S, Gao; Dadashkarimi, Javid; As, Greene; Mn, Spann; Noble, Stephanie; Lake, Evelyn; Krystal, John H.; Rt, Constable; Scheinost, Dustin

doi:10.1101/638825

Cited by 8 publications

(6 citation statements)

References 27 publications

(29 reference statements)

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“…Through these approaches, functional connectivity can be used to predict individual differences in cognition, personality, as well as psychiatric and behavioral problems (19)(20)(21)(22)(23)(24). These models have been used to establish the neurobiological correlates of attention (25,26), memory (27), anxiety (28), depression (29), psychosis (30), and substance abuse (31,32). When developing predictive models, it is crucial to ensure that they are not only accurate within circumscribed groups but that they can also generalize to other populations.…”

Section: Introductionmentioning

confidence: 99%

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

Dhamala

Ooi

Chen

et al. 2022

Preprint

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Background: Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. Methods: In this work, we used predictive modelling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development study (n=5260; 2571 females). Results: We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females, but only predict internalizing behaviors in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks including default, limbic, control, and dorsal attention networks are associated with internalizing and externalizing behaviors as well as attentional deficits. Conclusions: Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. These results highlight the need to consider factors beyond just neurobiology in the diagnosis and treatment of psychiatric illnesses.

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Section: Introductionmentioning

confidence: 99%

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

Dhamala

Ooi

Chen

et al. 2022

Preprint

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“…Among these mental disorders, similar neurocognitive deficits (1) and underlying genetic basis (2, 3) have been found. For instance, differences in brain networks associated with memory and cognitive control have been reported in these disorders, particularly those in prefrontal, default mode, and limbic networks (4,5). Notably, symptom profiles differ across these disorders, with SZ high in psychotic features, BD increased in emotional dysfunction and ADHD high in externalizing behaviors.…”

Section: Introductionmentioning

confidence: 99%

Segmental abnormalities of superior longitudinal fasciculus microstructure in patients with schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder: An automated fiber quantification tractography study

Jin

Zuo

et al. 2022

Front. Psychiatry

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IntroductionSuperior longitudinal fasciculus (SLF) is a white matter (WM) tract that connects the frontal, parietal and temporal lobes. SLF integrity has been widely assessed in neuroimaging studies of psychiatric disorders, such as schizophrenia (SZ), bipolar disorder (BD), and attention-deficit/hyperactivity disorder (ADHD). However, prior studies have revealed inconsistent findings and comparisons across disorders have not been fully examined.MethodsHere, we obtained data for 113 patients (38 patients with SZ, 40 with BD, 35 with ADHD) and 94 healthy controls from the UCLA Consortium for Neuropsychiatric Phenomic LA5c dataset. We assessed the integrity of 20 major WM tracts with a novel segmentation method by automating fiber tract quantification (AFQ). The AFQ divides each tract into 100 equal parts along the direction of travel, with fractional anisotropy (FA) of each part taken as a characteristic. Differences in FA among the four groups were examined.ResultsCompared to healthy controls, patients with SZ showed significantly lower FA in the second half (51–100 parts) of the SLF. No differences were found between BD and healthy controls, nor between ADHD and healthy controls. Results also demonstrated that patients with SZ showed FA reduction in the second half of the SLF relative to patients with BP. Moreover, greater FA in patients in SLF was positively correlated with the manic-hostility score of the Brief Psychiatry Rating scale.DiscussionThese findings indicated that differences in focal changes in SLF might be a key neurobiological abnormality contributing to characterization of these psychiatric disorders.

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“…Along with the exploration of technology, an increasing number of researchers started to concentrate on the relationship between brain connectivity and brain function or diseases. The most extensively studied brain functions included cognition [21,22], memory [23,24], and language [25,26]. In terms of diseases, many researchers have investigated the association between brain connectivity and epilepsy [27,28], and stroke [29,30].…”

Section: Discussionmentioning

confidence: 99%

Research Trends and Hotspots on Connectomes from 2005 to 2021: A Bibliometric and Latent Dirichlet Allocation Application Study

Yan

Fan

Liao

et al. 2022

Preprint

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Background This study aimed to conduct a bibliometric analysis of publications on connectomes and illustrate its trends and hotspots using a machine-learning-based text mining algorithm. Methods Documents were retrieved from the Web of Science Core Collection (WoSCC) database and analyzed in Rstudio 1.3.1. Through quantitative and qualitative methods, the top 20 productive and impact academic journals and authors in the field of connectomes were compared in terms of the total number of publications and h_inde over time. Meanwhile, the countries/regions and institutions involved in connectome research were compared, as well as their scientific collaboration. The study analyzed topics and research trends by R package “bibliometrix”. The major topics of connectomes were classified by Latent Dirichlet allocation (LDA). Results The entire criteria were met by 6617 publications. Neuroimage outperformed all other relevant journals in terms of publication volume (680 articles) and impact factor (h index:98). The majority of articles were published by developed countries, with the United States having the most. The University of Pennsylvania and Beijing Normal University were the two most productive institutions. Neuroimaging analysis technology and brain functions and diseases were the two major topics of connectome research. The combination with machining learning or deep learning, as well as the focus on deep brain stimulation and hippocampus has been the recent trend in connectome research. Conclusions This study offers a comprehensive overview of connectome research and provides researchers with critical information for understanding the recent trends and hotspots of connectomes.

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Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease

Cited by 8 publications

References 27 publications

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

Segmental abnormalities of superior longitudinal fasciculus microstructure in patients with schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder: An automated fiber quantification tractography study

Research Trends and Hotspots on Connectomes from 2005 to 2021: A Bibliometric and Latent Dirichlet Allocation Application Study

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