Prantik Kundu

Frontiers in Psychiatry

Directory of Open Access Journals

TYPE EditorialPUBLISHED 13 April 2023DOI 10.3389/fpsyt.2023.1174588OPEN ACCESSEDITED AND REVIEWED BYStefan Borgwardt,University of Lübeck, Germany*CORRESPONDENCEPrantik Kundupkundu@ceretype.comSPECIALTY SECTIONThis article was submitted toNeuroimaging,a section of the journalFrontiers in PsychiatryRECEIVED 26 February 2023ACCEPTED 28 February 2023PUBLISHED 13 April 2023CITATIONKundu P (2023) Editorial: Functionalneuroimaging in psychiatric practice: How farhave we come? Front. Psychiatry 14:1174588.doi: 10.3389/fpsyt.2023.1174588COPYRIGHT© 2023 Kundu. This is an open-access articledistributed under the terms of the CreativeCommons Attribution License (CC BY). The use,distribution or reproduction in other forums ispermitted, provided the original author(s) andthe copyright owner(s) are credited and thatthe original publication in this journal is cited, inaccordance with accepted academic practice.No use, distribution or reproduction ispermitted which does not comply with theseterms.Editorial: Functionalneuroimaging in psychiatricpractice: How far have we come?Prantik Kundu1,2,3*1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States,2Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, 3CeretypeNeuromedicine Inc., Cambridge, MA, United StatesKEYWORDSfMRI, connectivity, neuropsychiatric disease, artificial intelligence, diagnosis, imaging,treatment monitoringEditorial on the Research TopicFunctional neuroimaging in psychiatric practice: How far have we come?In this Research Topic, “Functional neuroimaging in psychiatric practice: How farhave we come?” we saw five studies applying state-of-the-art neuroimaging methodologyto address diagnosis and characterization of psychiatric disease. The findings pertaineddiversely to ADHD, schizophrenia, bipolar disorder, treatment resistant depression, bulimianervosa, suicidality, and the prodrome of Alzheimer’s disease (AD). Functional connectivityestimated from functional MRI (fMRI) was the primary methodology in all studies. Inaddition, Wang et al. utilized a PET marker for Amyloid-β (Aβ) to assess the Alzheimer’sendophenotype in terms of resting-state functional connectivity patterns differentiatedbetween Aβ+ and Aβ- individuals. The study on bulimia nervosa by Li et al. used structuralMRI as a correlate of functional connectivity. Demonstrating the application of functionalconnectivity to study the treatment effect of a novel device, Sun et al. presented resting-state fMRI evidence for Auricular Vagus Nerve Stimulation. And, representing the advancedapproach of deep learning, Oliveira-Saraiva and Ferreira present cross-diagnostic predictivemodeling to characterize similarities and differences between diagnostic groups.The articles in this Research Topic recapitulate for psychiatry the precedent fromneuroimaging that functional connectivity based on fMRI is essential to characterizing brainfunction (1). These studies demonstrate the power of this technique is in its incredibleversatility for imaging diverse patient populations using the same imaging protocol. Thus,resting-state fMRI should be a component for any brain imaging strategy in neuropsychiatry.Similarly, functional MRI data generally and functional connectivity measurementsspecifically served as inputs to a diverse range of downstream statistical and machinelearning analyses, suggesting recurring value. Notably, task-based fMRI was not represented,reflecting a trend of decreasing utility of this classical approach. This is remarkable giventhe importance of task-based fMRI in the first generation of fMRI study to elucidate neuralcorrelates of function and modulation in drugs and diseases. This transition has beenfacilitated as an impact on psychiatry from the series of discoveries over the past decadeon the correspondence of functional brain organization across task and resting states (2).With further effort, the neurobiological state of multiple domains of brain function may beclinically evaluated based on a single resting-state fMRI exam (Figure 1) to map a patient’sresting-state functional networks and identify their domains of function by referencingmeta-analytic atlases of functional organization based on thousands of task-fMRI studies, sothat domain-associated patient networks can be evaluated with biomarkers of endogenousactivity such as those described in this series to determine diagnosis or treatment response.Frontiers in Psychiatry 01 frontiersin.orgKundu 10.3389/fpsyt.2023.1174588In addition to estimates of inter-areal functional connectivity,resting-state fMRI data was used to characterize local functionalarchitecture of brain tissues. The Amplitude of Low FrequencyFluctuations (ALFF) and the normalized variant fractional ALFF(fALFF) measure the amplitude of endogenous temporal signalsin the frequency range most associated with resting-state fMRI.Conventionally, amplitude-basedmetrics of fMRI quantify the levelof neural activation due to experimental task conditions; ALFFmeasures amplitude of neuronal activity in the absence of task(3). This measure of activity at “baseline” showed differences: intreatment resistant depression in response to auricular vagus nervestimulation in Sun et al.; in women with past suicide attemptsvs. without in Liu et al.; predictive of Aβ+ status (n.b. not cross-validated) in Wang et al.. Another use of resting-state fMRI timeseries data is in the estimation of local functional connectivityor regional homogeneity (ReHo) based on the computationof Kendall’s [multivariate] correlation coefficient over sets oftimeseries from an anatomical region (4). Wang et al. comparedALFF and ReHo in their association with Aβ+ status using aprediction framework, reporting that ReHo had moderate AUCthat was statistically significant but less than AUC for ALFF.Analytic modeling of fMRI data using learning methods (e.g.,support vector machine, SVM) have been studied for severalyears (5). Oliveira-Saraiva and Ferreira apply a state-of-the-artFIGURE 1Association of individual subject functional networks to functional domains. Functional networks from one individual are derived from oneresting-state fMRI dataset (8 min. acquisition) acquired with high-reproducibility fMRI strategy∧7,8 (multi-band multi-echo fMRI). The association tofunctional domains is based on automated lookup in meta-analytic database (image-based query) representing task-activation foci from over 15,000neuroimaging studies conducted over 30 years (neurosynth.org). Association of individual subject network imaging data to functional domain madevia automatically synthesized image representing functional domain using forward-inference model (frequentist) model.artificial intelligence (AI) technique. They first use dual-regressionto project a set of population-level maps of brain circuits (e.g.,default mode network, motor network, etc.) onto individual subjectdata to produce the time series of those circuits. Then theycomputed the connectome among those brain circuits based on thecorrelations between their time series, expressed as a connectivitymatrix. This produced a highly compressed representation ofwhole-brain connectivity for a subject, expressed in only 91parameters. A convolutional neural network autoencoder wasthen used to “discover” a common set of latent rules expressedwithin all subject connectivity matrices. These rules are intrinsicallyvalidated by their ability to compress the data [further]. Aftertraining is complete, the rules encoded in the trained modelare applied to novel datasets to compress and then decompressthem. The information lost in the process reflects the accuracyof the learned model, quantified as the reconstruction error. Forexample, if a model were trained and/or tested with randomdata, reconstruction error is maximized since there are no latentpatterns to train on and/or test the rules against. If effectivelytrained on data that share a latent pattern—even a non-linearand layered one—the reconstruction error will be lower. Thepower of the neural network approach is its ability to findpredictive patterns more robustly than classical [regularized linear]methods such as SVM. The authors found that healthy subjectFrontiers in Psychiatry 02 frontiersin.orgKundu 10.3389/fpsyt.2023.1174588data had the lowest reconstruction error, indicating a relativelyhigh degree of consistency in brain circuitry of healthy individuals.Data of different diagnostic groups (e.g., ADHD, schizophrenia)each had significantly higher reconstruction error, suggestingparadoxically higher biological heterogeneity in patients with acommon diagnosis. The convolutional neural network approach isalso robust to nuisance effects by using techniques like dropoutwhich randomly “forgets” learned parameters during training toprevent overlearning noise features (6). This was demonstratedby a diagnosis-specific model (schizophrenia) trained and testedon the dataset of one study giving similar reconstruction errorwhen tested in separately acquired dataset. When evaluating thereconstruction error of connectivity matrices at the individualsubject level, those brain circuit relationships that emerged from theaverage of the diagnostic group was not distinguished in individualsubject connectivity, suggesting a tenuous relationship betweendiagnostic category and specific circuit pathophysiology.The results presented here reflect both a capability anda need for neuroimaging to be integrated into psychiatricpractice, given demonstrable capabilities in roles from diagnosisto treatment monitoring. Given the robust feasibility demonstratedhere especially of resting-state fMRI, a widely accessible technique,the bottleneck of adoption of neuroimaging in psychiatry isdecreasingly related to technical factors (7, 8). Thus, in the comingyears, the onus will be on field of psychiatry to adopt neuroimagingtechniques as part of improving care delivery toward reducingthe burden of psychiatric disease on patients and healthcaresystems alike.Author contributionsThe author confirms being the sole contributor of this work andhas approved it for publication.Conflict of interestPK was employed by Ceretype Neuromedicine Inc.Publisher’s noteAll claims expressed in this article are solely thoseof the authors and do not necessarily represent those oftheir affiliated organizations, or those of the publisher,the editors and the reviewers. Any product that may beevaluated in this article, or claim that may be made byits manufacturer, is not guaranteed or endorsed by thepublisher.References1. Woodward ND, Cascio CJ. Resting-state functional connectivityin psychiatric disorders. J Am Med Assoc Psychiatry. (2015)72:743. doi: 10.1001/jamapsychiatry.2015.04842. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, et al.Correspondence of the brain’s functional architecture during activation and rest. ProcNat Acad Sci USA. (2009) 106:13040–5. doi: 10.1073/pnas.09052671063. Zou Q-H, Zhu C-Z, Yang Y, Zuo X-N, Long X-Y, Cao Q-J, et al. Animproved approach to detection of amplitude of low-frequency fluctuation (ALFF)for resting-state fMRI: Fractional ALFF. J Neurosci Methods. (2008) 172:137–41. doi: 10.1016/j.jneumeth.2008.04.0124. Zang Y, Jiang T, Lu Y, He Y, Tian L. Regional homogeneity approach to fMRI dataanalysis. Neuroimage. (2004) 22:394–400. doi: 10.1016/j.neuroimage.2003.12.0305. Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR. Machinelearning in resting-state fMRI analysis. Magn Reson Imaging. (2019) 64:101–21. doi: 10.1016/j.mri.2019.05.0316. Shrestha A, Mahmood A. Review of deep learning algorithms andarchitectures. IEEE Access. (2019) 7:53040–65. doi: 10.1109/ACCESS.2019.29122007. Kundu P, Inati SJ, Evans JW, Luh W-M, Bandettini PA. Differentiating BOLDand non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage. (2012)60:1759–70. doi: 10.1016/j.neuroimage.2011.12.0288. Lynch CJ, Elbau I, Liston C. Improving precision functionalmapping routines with multi-echo fMRI. Curr Opin Behav Sci. (2021)40:113–9. doi: 10.1016/j.cobeha.2021.03.017Frontiers in Psychiatry 03 frontiersin.org

Editorial: Functional neuroimaging in psychiatric practice: How far have we come?

https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1174588/pdf

Editorial: Functional neuroimaging in psychiatric practice: How far have we come?

Abstract

Similar works

Full text

Available Versions

Directory of Open Access Journals