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Tyler A. Lesh

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

YNICL Journal 2022 Journal Article

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis

Jason Smucny
Ge Shi
Tyler A. Lesh
Cameron S. Carter
Ian Davidson

Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically small due to the considerable time, cost, and resources necessary to obtain them. A recently developed self-supervised learning method called Mixup may help overcome this challenge. In Mixup, the learner combines pairs of training instances to produce a virtual third instance that is a linear combination of the two instances and their labels. This procedure is also well-suited to the coregistered images typically found in fMRI datasets. Here we compared performance of a task fMRI-based deep learner with Mixup vs without Mixup on predicting response to treatment in recent onset psychosis. Whole brain fMRI time series data were extracted from a cognitive control task in 82 patients with recent onset psychosis and used to predict "Improver" (n = 47) vs "Non-Improver" (n = 35) status, with Improver defined as showing a 20 % reduction in total Brief Psychiatric Rating Scale score after 1 year of treatment. Mixup significantly improved performance (accuracy without Mixup: 76.5 % [95 % CI: 75.9-77.1 %]; accuracy with Mixup: 80.1 % [95 % CI: 79.4-80.8 %]). Ablation showed the improvement was due to improvement in both Improvers and Non-Improvers. These results suggest that using Mixup may significantly improve performance and reduce overfitting of fMRI-based prognostic deep learners and may also help overcome the small sample size challenge inherent to many neuroimaging datasets.

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YNICL Journal 2019 Journal Article

A multicenter study of ketamine effects on functional connectivity: Large scale network relationships, hubs and symptom mechanisms

Leah M. Fleming
Daniel C. Javitt
Cameron S. Carter
Joshua T. Kantrowitz
Ragy R. Girgis
Lawrence S. Kegeles
John D. Ragland
Richard J. Maddock

Ketamine is an uncompetitive N-methyl-d-aspartate (NMDA) glutamate receptor antagonist. It induces effects in healthy individuals that mimic symptoms associated with schizophrenia. We sought to root these experiences in altered brain function, specifically aberrant resting state functional connectivity (rsfMRI). In the present study, we acquired rsfMRI data under ketamine and placebo in a between-subjects design and analyzed seed-based measures of rsfMRI using large-scale networks, dorsolateral prefrontal cortex (DLPFC) and sub-nuclei of the thalamus. We found ketamine-induced alterations in rsfMRI connectivity similar to those seen in patients with schizophrenia, some changes that may be more comparable to early stages of schizophrenia, and other connectivity signatures seen in patients that ketamine did not recreate. We do not find any circuits from our regions of interest that correlates with positive symptoms of schizophrenia in our sample, although we find that DLPFC connectivity with ACC does correlate with a mood measure. These results provide support for ketamine's use as a model of certain biomarkers of schizophrenia, particularly for early or at-risk patients.

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YNICL Journal 2017 Journal Article

Functional network changes and cognitive control in schizophrenia

Kimberly L. Ray
Tyler A. Lesh
Amber M. Howell
Taylor P. Salo
J. Daniel Ragland
Angus W. MacDonald
James M. Gold
Steven M. Silverstein

Cognitive control is a cognitive and neural mechanism that contributes to managing the complex demands of day-to-day life. Studies have suggested that functional impairments in cognitive control associated brain circuitry contribute to a broad range of higher cognitive deficits in schizophrenia. To examine this issue, we assessed functional connectivity networks in healthy adults and individuals with schizophrenia performing tasks from two distinct cognitive domains that varied in demands for cognitive control, the RiSE episodic memory task and DPX goal maintenance task. We characterized general and cognitive control-specific effects of schizophrenia on functional connectivity within an expanded frontal parietal network (FPN) and quantified network topology properties using graph analysis. Using the network based statistic (NBS), we observed greater network functional connectivity in cognitive control demanding conditions during both tasks in both groups in the FPN, and demonstrated cognitive control FPN specificity against a task independent auditory network. NBS analyses also revealed widespread connectivity deficits in schizophrenia patients across all tasks. Furthermore, quantitative changes in network topology associated with diagnostic status and task demand were observed. The present findings, in an analysis that was limited to correct trials only, ensuring that subjects are on task, provide critical insights into network connections crucial for cognitive control and the manner in which brain networks reorganize to support such control. Impairments in this mechanism are present in schizophrenia and these results highlight how cognitive control deficits contribute to the pathophysiology of this illness.

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YNICL Journal 2013 Journal Article

Proactive and reactive cognitive control and dorsolateral prefrontal cortex dysfunction in first episode schizophrenia

Tyler A. Lesh
Andrew J. Westphal
Tara A. Niendam
Jong H. Yoon
Michael J. Minzenberg
J. Daniel Ragland
Marjorie Solomon
Cameron S. Carter

Cognitive control deficits have been consistently documented in patients with schizophrenia. Recent work in cognitive neuroscience has hypothesized a distinction between two theoretically separable modes of cognitive control-reactive and proactive. However, it remains unclear the extent to which these processes are uniquely associated with dysfunctional neural recruitment in individuals with schizophrenia. This functional magnetic resonance imaging (fMRI) study utilized the color word Stroop task and AX Continuous Performance Task (AX-CPT) to tap reactive and proactive control processes, respectively, in a sample of 54 healthy controls and 43 patients with first episode schizophrenia. Healthy controls demonstrated robust dorsolateral prefrontal, anterior cingulate, and parietal cortex activity on both tasks. In contrast, patients with schizophrenia did not show any significant activation during proactive control, while showing activation similar to control subjects during reactive control. Critically, an interaction analysis showed that the degree to which prefrontal activity was reduced in patients versus controls depended on the type of control process engaged. Controls showed increased dorsolateral prefrontal cortex (DLPFC) and parietal activity in the proactive compared to the reactive control task, whereas patients with schizophrenia did not demonstrate this increase. Additionally, patients' DLPFC activity and performance during proactive control was associated with disorganization symptoms, while no reactive control measures showed this association. Proactive control processes and concomitant dysfunctional recruitment of DLPFC represent robust features of schizophrenia that are also directly associated with symptoms of disorganization.

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