Connectome-based models can predict early symptom improvement in major depressive disorder

Ju, Yumeng; Horien, Corey; Chen, Wentao; Guo, Weilong; Lu, Xiaowen; Sun, Jin; Dong, Qiangli; Liu, Bangshan; Liu, Jin; Yan, Danfeng; Wang, Mi; Zhang, Liang; Guo, Hua; Zhao, Futao; Zhang, Yan; Shen, Xilin; Constable, R. Todd; Li, Lingjiang

doi:10.1016/j.jad.2020.04.028

Cited by 44 publications

(30 citation statements)

References 69 publications

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“…However, the ANOVA was run assuming unequal variance, and supplementary post hoc analyses also suggest that unequal variance did not drive the study findings (see the supplemental results in the online supplement). Nonetheless, future research in this area should consider use of larger, more equally distributed groups (if possible) and/or more advanced prediction frameworks, such as connectome-based predictive Am J Psychiatry 178:4, April 2021 ajp.psychiatryonline.org 349 modeling (45,46). While the present findings and interpretations should be viewed in light of the fact that several findings did not survive a more conservative voxel-wise threshold, the findings highlight key regions (e.g., NAcc, sgACC) that may be targeted a priori in future research and replication studies.…”

Section: Discussionmentioning

confidence: 99%

Association of Neural Reward Circuitry Function With Response to Psychotherapy in Youths With Anxiety Disorders

Sequeira

Silk

Ladouceur

et al. 2021

AJP

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Section: Discussionmentioning

confidence: 99%

Association of Neural Reward Circuitry Function With Response to Psychotherapy in Youths With Anxiety Disorders

Sequeira

Silk

Ladouceur

et al. 2021

AJP

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“…Connectome-based predictive modeling has been successful in predicting many different measures across clinical populations with heterogeneous presentations and symptoms. Specifically, CPM has been used to predict symptom improvement after pharmacotherapy in major depressive disorder (Ju et al, 2020), waist circumference and fasting insulin in overweight and obese individuals (Farruggia et al, 2020), global cognition in samples of individuals with mild cognitive impairment and Alzheimer's disease (Lin et al, 2018), and selfreported executive dysfunction and memory in patients with breast cancer (Henneghan et al, 2020). Of note, although some of these studies included repeated, longitudinal assessment of networks (Henneghan et al, 2020;Ju et al, 2020), none of these studies in clinical populations included independent samples to test the generalizability of identified networks.…”

Section: Discussionmentioning

confidence: 99%

Employing connectome-based models to predict working memory in multiple sclerosis

Manglani

Fountain-Zaragoza

Shankar

et al. 2021

Preprint

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Background: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory, and the search for neural correlates of working memory in circumscribed areas has yielded inconclusive findings. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual-level working memory in this population. Methods: Here, we applied connectome-based predictive modeling to functional MRI data from working memory tasks in two independent samples with relapsing-remitting MS. In the internal validation sample (ninternal = 36), functional connectivity data were used to train a model through cross-validation to predict accuracy on the Paced Visual Serial Addition Test, a gold-standard measure of working memory in MS. We then tested its ability to predict performance on the N-back working memory task in the external validation sample (nexternal = 36). Results: The resulting model successfully predicted working memory in the internal validation sample but did not extend to the external sample. We also tested the generalizability of an existing model of working memory derived in healthy young adults to people with MS. It showed successful prediction in both MS samples, demonstrating its translational potential. We qualitatively explored differences between the healthy and MS models in intra- and inter-network connectivity amongst canonical networks. Discussion: These findings suggest that connectome-based predictive models derived in people with MS may have limited generalizability. Instead, models identified in healthy individuals may offer superior generalizability to clinical samples, such as MS, and may serve as more useful targets for intervention.

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“…If a participant’s scan had a mean FFD above a threshold, it was designated as a “high-motion” scan; if it was below the threshold, it was considered “low-motion.” Mean FFD thresholds of 0.10, 0.15, and 0.20 mm were used, as thresholds of this magnitude have been shown to limit the impact of motion 21 , 33 while allowing for sample sizes of adequate size in children/adolescents 33 , 34 and in those with a disorder 35 , 36 , 57 . We used a Chi-square test of association to determine if there was a significant difference in the number of high and low-motion scans; significance was assessed at a P -value < 0.05 after correcting for multiple comparisons (Benjamini–Hochberg procedure 58 ).…”

Section: Methodsmentioning

confidence: 99%

Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol

Horien

Fontenelle²,

Joseph³

et al. 2020

Sci Rep

Self Cite

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Performing functional magnetic resonance imaging (fMRI) scans of children can be a difficult task, as participants tend to move while being scanned. Head motion represents a significant confound in fMRI connectivity analyses. One approach to limit motion has been to use shorter MRI protocols, though this reduces the reliability of results. Hence, there is a need to implement methods to achieve high-quality, low-motion data while not sacrificing data quantity. Here we show that by using a mock scan protocol prior to a scan, in conjunction with other in-scan steps (weighted blanket and incentive system), it is possible to achieve low-motion fMRI data in pediatric participants (age range: 7–17 years old) undergoing a 60 min MRI session. We also observe that motion is low during the MRI protocol in a separate replication group of participants, including some with autism spectrum disorder. Collectively, the results indicate it is possible to conduct long scan protocols in difficult-to-scan populations and still achieve high-quality data, thus potentially allowing more reliable fMRI findings.

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Connectome-based models can predict early symptom improvement in major depressive disorder

Cited by 44 publications

References 69 publications

Association of Neural Reward Circuitry Function With Response to Psychotherapy in Youths With Anxiety Disorders

Association of Neural Reward Circuitry Function With Response to Psychotherapy in Youths With Anxiety Disorders

Employing connectome-based models to predict working memory in multiple sclerosis

Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol

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