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Thai M, Olson EA, Nickels S, Dillon DG, Webb CA, Ren B, Killgore WDS, Rauch SL, Rosso IM, Pizzagalli DA. Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression. Transl Psychiatry 2024; 14:303. [PMID: 39043642 PMCID: PMC11266709 DOI: 10.1038/s41398-024-03020-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 07/25/2024] Open
Abstract
Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.
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Affiliation(s)
- Michelle Thai
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Elizabeth A Olson
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Stefanie Nickels
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Daniel G Dillon
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Christian A Webb
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric Biostatistics Laboratory, McLean Hospital, Belmont, MA, USA
| | - William D S Killgore
- Department of Psychiatry, University of Arizona College of Medicine, Tucson, AZ, USA
| | - Scott L Rauch
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Imaging Center, McLean Hospital, Belmont, MA, USA
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Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
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Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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Copa D, Erritzoe D, Giribaldi B, Nutt D, Carhart-Harris R, Tagliazucchi E. Predicting the outcome of psilocybin treatment for depression from baseline fMRI functional connectivity. J Affect Disord 2024; 353:60-69. [PMID: 38423367 DOI: 10.1016/j.jad.2024.02.089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Psilocybin is a serotonergic psychedelic drug under assessment as a potential therapy for treatment-resistant and major depression. Heterogeneous treatment responses raise interest in predicting the outcome from baseline data. METHODS A machine learning pipeline was implemented to investigate baseline resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI) as a predictor of symptom severity in psilocybin monotherapy for treatment-resistant depression (16 patients administered two 5 mg capsules followed by 25 mg, separated by one week). Generalizability was tested in a sample of 22 patients who participated in a psilocybin vs. escitalopram trial for moderate-to-severe major depression (two separate doses of 25 mg of psilocybin 3 weeks apart plus 6 weeks of daily placebo vs. two separate doses of 1 mg of psilocybin 3 weeks apart plus 6 weeks of daily oral escitalopram). The analysis was repeated using both samples combined. RESULTS Functional connectivity of visual, default mode and executive networks predicted early symptom improvement, while the salience network predicted responders up to 24 weeks after treatment (accuracy≈0.9). Generalization performance was borderline significant. Consistent results were obtained from the combined sample analysis. Fronto-occipital and fronto-temporal coupling predicted early and late symptom reduction, respectively. LIMITATIONS The number of participants and differences between the two datasets limit the generalizability of the findings, while the lack of a placebo arm limits their specificity. CONCLUSIONS Baseline neurophysiological measurements can predict the outcome of psilocybin treatment for depression. Future research based on larger datasets should strive to assess the generalizability of these predictions.
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Affiliation(s)
- Débora Copa
- Universidad de Buenos Aires, Facultad de Ingeniería, Instituto de Bioingeniería, Buenos Aires, Argentina.
| | - David Erritzoe
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom
| | - Bruna Giribaldi
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom
| | - David Nutt
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom
| | - Robin Carhart-Harris
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom; Psychedelics Division, Neuroscape, Department of Neurology, University of California, San Francisco, USA
| | - Enzo Tagliazucchi
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Ciudad Universitaria, Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Instituto de Física Interdisciplinaria y Aplicada (INFINA), Ciudad Universitaria, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
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Meinke C, Lueken U, Walter H, Hilbert K. Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis. Neurosci Biobehav Rev 2024; 160:105640. [PMID: 38548002 DOI: 10.1016/j.neubiorev.2024.105640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 02/29/2024] [Accepted: 03/21/2024] [Indexed: 04/07/2024]
Abstract
Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.
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Affiliation(s)
- Charlotte Meinke
- Department of Psychology, Humboldt-Universität zu Berlin, Germany.
| | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, Germany; German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Germany.
| | - Henrik Walter
- Charité Universtätsmedizin Berlin, corporate member of FU Berlin and Humboldt Universität zu Berlin, Department of Psychiatrie and Psychotherapy, CCM, Germany.
| | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Germany; Department of Psychology, Health and Medical University Erfurt, Germany.
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Domke AK, Hempel M, Hartling C, Stippl A, Carstens L, Gruzman R, Herrera Melendez AL, Bajbouj M, Gärtner M, Grimm S. Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms. Eur Arch Psychiatry Clin Neurosci 2023; 273:1489-1499. [PMID: 36715751 PMCID: PMC10465635 DOI: 10.1007/s00406-023-01552-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/09/2023] [Indexed: 01/31/2023]
Abstract
Electroconvulsive therapy (ECT) is one of the most effective treatments for treatment-resistant depression. However, the underlying mechanisms of action are not yet fully understood. The investigation of depression-specific networks using resting-state fMRI and the relation to differential symptom improvement might be an innovative approach providing new insights into the underlying processes. In this naturalistic study, we investigated the relationship between changes in resting-state functional connectivity (rsFC) and symptom improvement after ECT in 21 patients with treatment-resistant depression. We investigated rsFC before and after ECT and focused our analyses on FC changes directly related to symptom reduction and on FC at baseline to identify neural targets that might predict individual clinical responses to ECT. Additional analyses were performed to identify the direct relationship between rsFC change and symptom dimensions such as sadness, negative thoughts, detachment, and neurovegetative symptoms. An increase in rsFC between the left amygdala and left dorsolateral prefrontal cortex (DLPFC) after ECT was related to overall symptom reduction (Bonferroni-corrected p = 0.033) as well as to a reduction in specific symptoms such as sadness (r = 0.524, uncorrected p = 0.014), negative thoughts (r = 0.700, Bonferroni-corrected p = 0.002) and detachment (r = 0.663, p = 0.004), but not in neurovegetative symptoms. Furthermore, high baseline rsFC between the left amygdala and the right frontal pole (FP) predicted treatment outcome (uncorrected p = 0.039). We conclude that changes in FC in regions of the limbic-prefrontal network are associated with symptom improvement, particularly in affective and cognitive dimensions. Frontal-limbic connectivity has the potential to predict symptom improvement after ECT. Further research combining functional imaging biomarkers and a symptom-based approach might be promising.
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Affiliation(s)
- Ann-Kathrin Domke
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Moritz Hempel
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Corinna Hartling
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Anna Stippl
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Luisa Carstens
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Rebecca Gruzman
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Ana Lucia Herrera Melendez
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Malek Bajbouj
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Matti Gärtner
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Simone Grimm
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland
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Mou J, Zheng T, Long Z, Mei L, Wang Y, Yuan Y, Guo X, Yang H, Gong Q, Qiu L. Sex differences of brain cortical structure in major depressive disorder. PSYCHORADIOLOGY 2023; 3:kkad014. [PMID: 38666130 PMCID: PMC10939343 DOI: 10.1093/psyrad/kkad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/16/2023] [Accepted: 09/01/2023] [Indexed: 04/28/2024]
Abstract
Background Major depressive disorder (MDD) has different clinical presentations in males and females. However, the neuroanatomical mechanisms underlying these sex differences are not fully understood. Objective The purpose of present study was to explore the sex differences in brain cortical thickness (CT) and surface area (SA) of MDD and the relationship between these differences and clinical manifestations in different gender. Methods High-resolution T1-weighted images were acquired from 61 patients with MDD and 61 healthy controls (36 females and 25 males, both). The sex differences in CT and SA were obtained using the FreeSurfer software and compared between every two groups by post hoc test. Spearman correlation analysis was also performed to explore the relationships between these regions and clinical characteristics. Results In male patients with MDD, the CT of the right precentral was thinner compared to female patients, although this did not survive Bonferroni correction. The SA of several regions, including right superior frontal, medial orbitofrontal gyrus, inferior frontal gyrus triangle, superior temporal, middle temporal, lateral occipital gyrus, and inferior parietal lobule in female patients with MDD was smaller than that in male patients (P < 0.01 after Bonferroni correction). In female patients, the SA of the right superior temporal (r = 0.438, P = 0.008), middle temporal (r = 0.340, P = 0.043), and lateral occipital gyrus (r = 0.372, P = 0.025) were positively correlated with illness duration. Conclusion The current study provides evidence of sex differences in CT and SA in patients with MDD, which may improve our understanding of the sex-specific neuroanatomical changes in the development of MDD.
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Affiliation(s)
- Jingping Mou
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
- Department of Radiology, the First People's Hospital of Yibin, Yibin 644000, China
| | - Ting Zheng
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Zhiliang Long
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Lan Mei
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
| | - Yuting Wang
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Yizhi Yuan
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
| | - Xin Guo
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Hongli Yang
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China
| | - Lihua Qiu
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
- Research Center of Neuroimaging big data, the Second People's Hospital of Yibin,Yibin 644000, China
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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8
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Cao D, Liu Q, Zhang J, Li J, Jiang T. State-specific modulation of mood using intracranial electrical stimulation of the orbitofrontal cortex. Brain Stimul 2023; 16:1112-1122. [PMID: 37467951 DOI: 10.1016/j.brs.2023.07.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/09/2023] [Accepted: 07/13/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Orbitofrontal cortex (OFC) is a promising target for intracranial electrical stimulation (iES) aimed at improving mood states. However, knowledge gaps remain regarding the underlying neural mechanisms of iES effects, such as the effect of the OFC target in comparison with other emotional network targets, the impact of brain state at the time of stimulation, and the neural response induced by iES at both local and network scales. OBJECTIVE Our study aims to address the neural mechanisms underlying the effects of iES in improving mood states. METHODS We conducted a study in 24 epilepsy patients who received iES through implanted electrodes in the emotional network and compared the effects of iES on multiple targets in the emotional network. RESULTS We found that only iES applied to the orbitofrontal cortex (OFC) led to mood improvement and changes in neural activity. We also observed that iES to the OFC suppressed the delta-theta power when the brain was in a low mood state. Moreover, the iES to the OFC decreased delta-theta power and increased gamma power at local regions within the emotional network, and enhanced the information flow through the frequency domain among regions within the emotional network. CONCLUSIONS These findings provide insight into the neural correlates of iES-induced mood improvement and support the potential of iES as a therapeutic intervention for mood disorders.
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Affiliation(s)
- Dan Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Qihong Liu
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jin Li
- School of Psychology, Capital Normal University, Beijing, 100048, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Research Center for Augmented Intelligence, Zhejiang Lab, 311100, Hangzhou, China.
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Alterations in EEG functional connectivity in individuals with depression: A systematic review. J Affect Disord 2023; 328:287-302. [PMID: 36801418 DOI: 10.1016/j.jad.2023.01.126] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/22/2023] [Accepted: 01/30/2023] [Indexed: 02/19/2023]
Abstract
The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.
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Kyuragi Y, Oishi N, Yamasaki S, Hazama M, Miyata J, Shibata M, Fujiwara H, Fushimi Y, Murai T, Suwa T. Information flow and dynamic functional connectivity during electroconvulsive therapy in patients with depression. J Affect Disord 2023; 328:141-152. [PMID: 36801417 DOI: 10.1016/j.jad.2023.02.060] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
BACKGROUND Electroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses. METHODS We performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression. RESULTS We demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment. LIMITATIONS First, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference. CONCLUSIONS These results show the specific properties of functional brain connectivity.
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Affiliation(s)
- Yusuke Kyuragi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Naoya Oishi
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto 606-8397, Japan.
| | - Shimpei Yamasaki
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Masaaki Hazama
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Mami Shibata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Hironobu Fujiwara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Artificial Intelligence Ethics and Society Team, RIKEN Center for Advanced Intelligence Project, Saitama 351-0198, Japan; The General Research Division, Research Center on Ethical, Legal and Social Issues, Osaka University, Osaka 565-0871, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Taro Suwa
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
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11
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Petrican R, Fornito A. Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing. Dev Cogn Neurosci 2023; 60:101229. [PMID: 36947895 PMCID: PMC10041470 DOI: 10.1016/j.dcn.2023.101229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
In adulthood, stress exposure and genetic risk heighten psychological vulnerability by accelerating neurobiological senescence. To investigate whether molecular and brain network maturation processes play a similar role in adolescence, we analysed genetic, as well as longitudinal task neuroimaging (inhibitory control, incentive processing) and early life adversity (i.e., material deprivation, violence) data from the Adolescent Brain and Cognitive Development study (N = 980, age range: 9-13 years). Genetic risk was estimated separately for Major Depressive Disorder (MDD) and Alzheimer's Disease (AD), two pathologies linked to stress exposure and allegedly sharing a causal connection (MDD-to-AD). Adversity and genetic risk for MDD/AD jointly predicted functional network segregation patterns suggestive of accelerated (GABA-linked) visual/attentional, but delayed (dopamine [D2]/glutamate [GLU5R]-linked) somatomotor/association system development. A positive relationship between brain maturation and psychopathology emerged only among the less vulnerable adolescents, thereby implying that normatively maladaptive neurodevelopmental alterations could foster adjustment among the more exposed and genetically more stress susceptible youths. Transcriptomic analyses suggested that sensitivity to stress may underpin the joint neurodevelopmental effect of adversity and genetic risk for MDD/AD, in line with the proposed role of negative emotionality as a precursor to AD, likely to account for the alleged causal impact of MDD on dementia onset.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool L69 7ZA, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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12
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Fu Z, Abbott CC, Sui J, Calhoun VD. Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes. Front Pharmacol 2023; 14:1102413. [PMID: 36755955 PMCID: PMC9899999 DOI: 10.3389/fphar.2023.1102413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain's reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10-3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10-21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Christopher C. Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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13
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Karvelis P, Charlton CE, Allohverdi SG, Bedford P, Hauke DJ, Diaconescu AO. Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review. Netw Neurosci 2022; 6:1066-1103. [PMID: 38800454 PMCID: PMC11117101 DOI: 10.1162/netn_a_00233] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/14/2022] [Indexed: 05/29/2024] Open
Abstract
Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.
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Affiliation(s)
- Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Colleen E. Charlton
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Shona G. Allohverdi
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Peter Bedford
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Daniel J. Hauke
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O. Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- University of Toronto, Department of Psychiatry, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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14
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Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
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Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
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15
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Pang Y, Wei Q, Zhao S, Li N, Li Z, Lu F, Pang J, Zhang R, Wang K, Chu C, Tian Y, Wang J. Enhanced default mode network functional connectivity links with electroconvulsive therapy response in major depressive disorder. J Affect Disord 2022; 306:47-54. [PMID: 35304230 DOI: 10.1016/j.jad.2022.03.035] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 01/16/2022] [Accepted: 03/10/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is an effective neuromodulatory treatment for major depressive disorder (MDD), especially for cases resistant to antidepressant drugs. While the precise mechanisms underlying ECT efficacy are still unclear, it is speculated that ECT modulates brain connectivity. The current study aimed to investigate the longitudinal effects of ECT on resting-state functional connectivity (FC) in MDD patients and test if baseline FC can be used to predict therapeutic response. METHOD Resting-state functional magnetic resonance imaging data were collected at baseline and following ECT from 33 MDD patients. Whole-brain multi-voxel pattern analysis (MVPA) and region of interest-wise FC analysis were employed to fully investigate ECT effects on brain connectivity. Linear support vector regression was further utilized to predict the improvement in depressive symptoms based on baseline connectivity. RESULTS MVPA revealed a significant ECT effect on FC in the default mode network (DMN), central executive network (CEN), sensorimotor network (SMN), and cerebellar posterior lobe. The FCs within the DMN and between DMN and CEN were enhanced in patients after ECT, and the changed FC between the medial prefrontal cortex and ventrolateral prefrontal cortex was negatively correlated with depressive symptom improvement. Moreover, baseline FC within the DMN and between the DMN and CEN could effectively predict the improvement of depressive symptoms. CONCLUSIONS The findings suggest that the FCs within the DMN and between DMN and CEN may be critical therapeutic targets for effective antidepressant treatment as well as neuromarkers for predicting treatment response.
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Affiliation(s)
- Yajing Pang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Qiang Wei
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China
| | - Shanshan Zhao
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Nan Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Zhihui Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jianyue Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Kai Wang
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; China National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Yanghua Tian
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China.
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, 650500, China.
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16
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Jamieson AJ, Harrison BJ, Razi A, Davey CG. Rostral anterior cingulate network effective connectivity in depressed adolescents and associations with treatment response in a randomized controlled trial. Neuropsychopharmacology 2022; 47:1240-1248. [PMID: 34782701 PMCID: PMC9018815 DOI: 10.1038/s41386-021-01214-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/22/2021] [Accepted: 10/13/2021] [Indexed: 02/02/2023]
Abstract
The rostral anterior cingulate cortex (rACC) is consistently implicated in the neurobiology of depression. While the functional connectivity of the rACC has been previously associated with treatment response, there is a paucity of work investigating the specific directional interactions underpinning these associations. We compared the fMRI resting-state effective connectivity of 94 young people with major depressive disorder and 91 healthy controls. Following the fMRI scan, patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or a placebo. Using spectral dynamic causal modelling, we examined the effective connectivity of the rACC with eight other regions implicated in depression: the left and right anterior insular cortex (AIC), amygdalae, and dorsolateral prefrontal cortex (dlPFC); and in the midline, the subgenual (sgACC) and dorsal anterior cingulate cortex (dACC). Parametric empirical Bayes was used to compare baseline differences between controls and patients and responders and non-responders to treatment. Depressed patients demonstrated greater inhibitory connectivity from the rACC to the dlPFC, AIC, dACC and left amygdala. Moreover, treatment responders illustrated greater inhibitory connectivity from the rACC to dACC, greater excitatory connectivity from the dACC to sgACC and reduced inhibitory connectivity from the sgACC to amygdalae at baseline. The inhibitory hyperconnectivity of the rACC in depressed patients aligns with hypotheses concerning the dominance of the default mode network over other intrinsic brain networks. Surprisingly, treatment responders did not demonstrate connectivity which was more similar to healthy controls, but rather distinct alterations that may have predicated their enhanced treatment response.
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Affiliation(s)
- Alec J. Jamieson
- grid.1008.90000 0001 2179 088XMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton, VIC Australia
| | - Ben J. Harrison
- grid.1008.90000 0001 2179 088XMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton, VIC Australia
| | - Adeel Razi
- grid.1002.30000 0004 1936 7857Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Clayton, VIC Australia ,grid.450002.30000 0004 0611 8165Wellcome Centre for Human Neuroimaging, University College London, London, UK ,grid.440050.50000 0004 0408 2525CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, ON Canada
| | - Christopher G. Davey
- grid.1008.90000 0001 2179 088XDepartment of Psychiatry, The University of Melbourne, Parkville, VIC Australia
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17
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Li Y, Zhou Z, Li Q, Li T, Julian IN, Guo H, Chen J. Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network. Front Neurosci 2022; 16:889105. [PMID: 35578623 PMCID: PMC9106560 DOI: 10.3389/fnins.2022.889105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.
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Affiliation(s)
- Yao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Zihao Zhou
- College of Mathematics, Taiyuan University of Technology, Taiyuan, China
| | - Qifan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Tao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ibegbu Nnamdi Julian
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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18
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Fu Z, Sui J, Espinoza R, Narr K, Qi S, Sendi MSE, Abbot CC, Calhoun VD. Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:312-322. [PMID: 34303848 PMCID: PMC8783932 DOI: 10.1016/j.bpsc.2021.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy. METHODS In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis. RESULTS Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT. CONCLUSIONS These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Katherine Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mohammad S. E. Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Christopher C. Abbot
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States,Corresponding author: Dr. Christopher C. Abbott, Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States, , Phone: 505-272-0406
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States,Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut, United States,Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia, United States
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Liu W, Wang X, Hamalainen T, Cong F. Exploring Oscillatory Dysconnectivity Networks in Major Depression during Resting State Using Coupled Tensor Decomposition. IEEE Trans Biomed Eng 2022; 69:2691-2700. [PMID: 35180074 DOI: 10.1109/tbme.2022.3152413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time frequency connectivity subject are constructed for the healthy group and the major depression group. We assume that the two groups share the same features for group similarity and retain individual characteristics for group differences. Considering that the constructed tensors are nonnegative and the components in spectral and adjacency modes are partially consistent among the two groups, we formulate a double-coupled nonnegative tensor decomposition model. To reduce computational complexity, we introduce the lowrank approximation. Then, the fast hierarchical alternative least squares algorithm is applied for model optimization. After clustering analysis, we summarize four oscillatory networks characterizing the healthy group and four oscillatory networks characterizing the major depression group, respectively. The proposed model may reveal novel mechanisms of pathoconnectomics in MDD during rest, and it can be easily extended to other psychiatric disorders.
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20
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Regenold WT, Deng ZD, Lisanby SH. Noninvasive neuromodulation of the prefrontal cortex in mental health disorders. Neuropsychopharmacology 2022; 47:361-372. [PMID: 34272471 PMCID: PMC8617166 DOI: 10.1038/s41386-021-01094-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 02/06/2023]
Abstract
More than any other brain region, the prefrontal cortex (PFC) gives rise to the singularity of human experience. It is therefore frequently implicated in the most distinctly human of all disorders, those of mental health. Noninvasive neuromodulation, including electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS) among others, can-unlike pharmacotherapy-directly target the PFC and its neural circuits. Direct targeting enables significantly greater on-target therapeutic effects compared with off-target adverse effects. In contrast to invasive neuromodulation approaches, such as deep-brain stimulation (DBS), noninvasive neuromodulation can reversibly modulate neural activity from outside the scalp. This combination of direct targeting and reversibility enables noninvasive neuromodulation to iteratively change activity in the PFC and its neural circuits to reveal causal mechanisms of both disease processes and healthy function. When coupled with neuronavigation and neurophysiological readouts, noninvasive neuromodulation holds promise for personalizing PFC neuromodulation to relieve symptoms of mental health disorders by optimizing the function of the PFC and its neural circuits. ClinicalTrials.gov Identifier: NCT03191058.
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Affiliation(s)
- William T. Regenold
- grid.416868.50000 0004 0464 0574Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD USA
| | - Zhi-De Deng
- grid.416868.50000 0004 0464 0574Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD USA
| | - Sarah H. Lisanby
- grid.416868.50000 0004 0464 0574Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD USA
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21
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Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen JM, Hodge MR, Cler EA, Marcus DM, Barch DM, Yacoub E, Smith SM, Ugurbil K, Van Essen DC. The Human Connectome Project: A retrospective. Neuroimage 2021; 244:118543. [PMID: 34508893 PMCID: PMC9387634 DOI: 10.1016/j.neuroimage.2021.118543] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/13/2021] [Accepted: 08/30/2021] [Indexed: 01/21/2023] Open
Abstract
The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.
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Affiliation(s)
| | | | - Michael P Harms
- Washington University School of Medicine, St. Louis, MO, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre & NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | | | | | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | | | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Michael R Hodge
- Washington University School of Medicine, St. Louis, MO, USA
| | - Eileen A Cler
- Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel M Marcus
- Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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22
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Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression. Proc Natl Acad Sci U S A 2021; 118:2105730118. [PMID: 34593640 PMCID: PMC8501855 DOI: 10.1073/pnas.2105730118] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 12/24/2022] Open
Abstract
Research into neurobiology of depression primarily focuses on its complex psychological aspects. Here we propose an alternative approach and target sensorimotor alterations—a prominent but often neglected feature of depression. We demonstrated using resting-state functional MRI data and computational modeling that top-down and bottom-up information flow in sensory and motor cortices is altered with increasing depression severity in a way that is consistent with depression symptoms. Depression-associated changes were found to be consistent across sessions, amenable to treatment and of effect size sufficiently large to predict whether somebody has mild or severe depression. These results pave the way for an avenue of research into the neural underpinnings of mental health conditions. Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.
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23
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Porta-Casteràs D, Cano M, Camprodon JA, Loo C, Palao D, Soriano-Mas C, Cardoner N. A multimetric systematic review of fMRI findings in patients with MDD receiving ECT. Prog Neuropsychopharmacol Biol Psychiatry 2021; 108:110178. [PMID: 33197507 DOI: 10.1016/j.pnpbp.2020.110178] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/21/2020] [Accepted: 11/11/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is considered the most effective treatment for major depressive disorder (MDD). In recent years, the pursuit of the neurobiological mechanisms of ECT action has generated a significant amount of functional magnetic resonance imaging (fMRI) research. OBJECTIVE In this systematic review, we integrated all fMRI research in patients with MDD receiving ECT and, importantly, evaluated the level of convergence and replicability across multiple fMRI metrics. RESULTS While according to most studies changes in patients with MDD after ECT appear to be widely distributed across the brain, our multimetric review revealed specific changes involving functional connectivity increases in the superior and middle frontal gyri as the most replicated and across-modality convergent findings. Although this modulation of prefrontal connectivity was associated to ECT outcome, we also identified fMRI measurements of the subgenual anterior cingulate cortex as the fMRI signals most significantly linked to clinical response. CONCLUSION We identified specific prefrontal and cingulate territories which activity and connectivity with other brain regions is modulated by ECT, critically accounting for its mechanism of action.
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Affiliation(s)
- Daniel Porta-Casteràs
- Mental Health Department, Unitat de Neurociència Traslacional. Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Universitat Autònoma de Barcelona, CIBERSAM, Carlos III Health Institute, Bellaterra, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Marta Cano
- Mental Health Department, Unitat de Neurociència Traslacional. Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Universitat Autònoma de Barcelona, CIBERSAM, Carlos III Health Institute, Bellaterra, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain; Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain.
| | - Joan A Camprodon
- Laboratory for Neuropsychiatry and Neuromodulation, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Colleen Loo
- School of Psychiatry, University of New South Wales, Sydney, Australia; The Black Dog Institute, Sydney, Australia; St George Hospital, Sydney, Australia
| | - Diego Palao
- Mental Health Department, Unitat de Neurociència Traslacional. Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Universitat Autònoma de Barcelona, CIBERSAM, Carlos III Health Institute, Bellaterra, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Carles Soriano-Mas
- Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain; Department of Psychiatry, Bellvitge University Hospital-IDIBELL, CIBERSAM, Carlos III Health Institute, Barcelona, Spain
| | - Narcís Cardoner
- Mental Health Department, Unitat de Neurociència Traslacional. Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Universitat Autònoma de Barcelona, CIBERSAM, Carlos III Health Institute, Bellaterra, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain
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24
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Barredo J, Bozzay ML, Primack JM, Schatten HT, Armey MF, Carpenter LL, Philip NS. Translating Interventional Neuroscience to Suicide: It's About Time. Biol Psychiatry 2021; 89:1073-1083. [PMID: 33820628 PMCID: PMC8603185 DOI: 10.1016/j.biopsych.2021.01.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 12/26/2022]
Abstract
Despite significant advances in psychiatric and psychological treatment over the last 30 years, suicide deaths have increased. Unfortunately, neuroscience insights have yielded few translational interventions that specifically target suicidal thoughts and behaviors. In our view, this is attributable to two factors. The first factor is our limited integration of neurocircuitry models with contemporary suicide theory. The second challenge is inherent to the variable nature of suicide risk over time. Few interventional neuroscience studies evaluate how temporal fluctuations in risk affect treatment, despite evidence that temporality is a key component distinguishing suicide phenotypes. To wit, individual variability in risk trajectories may provide different treatment targets to engage as a patient moves between suicidal ideation and attempt. Here, we first review contemporary ideation-to-action theories of suicide from a neurobiological perspective, focusing on valence and executive function circuits and the key role of state-induced (e.g., within stressful contexts) functional modulation on longitudinal risk trajectories. We then describe neural correlates of suicide reduction following various interventions, ranging from circuit specific (i.e., transcranial magnetic stimulation) to broader pharmacological (i.e., ketamine, lithium) to psychological (i.e., brief cognitive therapy). We then introduce novel strategies for tracking risk in naturalistic settings and real time using ecological momentary interventions. We provide a critical integration of the literature focusing on the intersection between targets and temporality, and we conclude by proposing novel research designs integrating real-time and biologically based interventions to generate novel strategies for future suicide reduction research.
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Affiliation(s)
- Jennifer Barredo
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island; COBRE Center for Neuromodulation and Neuroimaging, Providence, Rhode Island.
| | - Melanie L Bozzay
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island
| | - Jennifer M Primack
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island; Psychosocial Research Program, Butler Hospital, Providence, Rhode Island; Providence VA Medical Center, Providence, Rhode Island
| | - Heather T Schatten
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island; Psychosocial Research Program, Butler Hospital, Providence, Rhode Island
| | - Michael F Armey
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island; Psychosocial Research Program, Butler Hospital, Providence, Rhode Island
| | - Linda L Carpenter
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island; COBRE Center for Neuromodulation and Neuroimaging, Providence, Rhode Island
| | - Noah S Philip
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island; COBRE Center for Neuromodulation and Neuroimaging, Providence, Rhode Island
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25
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Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 2021; 11:168. [PMID: 33723229 PMCID: PMC7960732 DOI: 10.1038/s41398-021-01286-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/05/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023] Open
Abstract
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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26
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Nakamura T, Tomita M, Horikawa N, Ishibashi M, Uematsu K, Hiraki T, Abe T, Uchimura N. Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression. Neuropsychopharmacol Rep 2021; 41:168-178. [PMID: 33615749 PMCID: PMC8340826 DOI: 10.1002/npr2.12165] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/21/2021] [Accepted: 01/23/2021] [Indexed: 12/28/2022] Open
Abstract
Aim Approximately one‐third of patients with major depressive disorder develop treatment‐resistant depression. One‐third of patients with treatment‐resistant depression demonstrate resistance to ketamine, which is a novel antidepressant effective for this disorder. The objective of this study was to examine the utility of resting‐state functional magnetic resonance imaging for the prediction of treatment response to ketamine in treatment‐resistant depression. Methods An exploratory seed‐based resting‐state functional magnetic resonance imaging analysis was performed to examine baseline resting‐state functional connectivity differences between ketamine responders and nonresponders before treatment with multiple intravenous ketamine infusions. Results Fifteen patients with treatment‐resistant depression received multiple intravenous subanesthetic (0.5 mg/kg/40 minutes) ketamine infusions, and nine were identified as responders. The exploratory resting‐state functional magnetic resonance imaging analysis identified a cluster of significant baseline resting‐state functional connectivity differences associating ketamine response between the amygdala and subgenual anterior cingulate gyrus in the right hemisphere. Using anatomical region of interest analysis of the resting‐state functional connectivity, ketamine response was predicted with 88.9% sensitivity and 100% specificity. The resting‐state functional connectivity of significant group differences between responders and nonresponders retained throughout the treatment were considered a trait‐like feature of heterogeneity in treatment‐resistant depression. Conclusion This study suggests the possible clinical utility of resting‐state functional magnetic resonance imaging for predicting the antidepressant effects of ketamine in treatment‐resistant depression patients and implicated resting‐state functional connectivity alterations to determine the trait‐like pathophysiology underlying treatment response heterogeneity in treatment‐resistant depression. This study illustrates that the alteration in the RSFC within the right AN in TRD patients reflects the antidepressant response to ketamine at baseline. The alteration remained throughout the 2‐week treatment with multiple ketamine infusions and seemed to reflect the trait‐like features underlying treatment heterogeneity in TRD. By employing an anatomical ROI of the sc/sgACC, the present study also suggests the possible clinical utility of the rsfMRI to predict the treatment response to ketamine in TRD patients.
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Affiliation(s)
- Tomoyuki Nakamura
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
| | - Masaru Tomita
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan.,Elm-tree Mental Clinic, Ogori City, Japan
| | - Naoki Horikawa
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan.,Nozoe Hills Hospital, Kurume City, Japan
| | - Masatoshi Ishibashi
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
| | - Ken Uematsu
- Uematsu Mental Clinic, Chikugo City, Japan.,Department of Pharmacology, Kurume University School of Medicine, Kurume City, Japan
| | - Teruyuki Hiraki
- Department of Anaesthesiology, Kurume University School of Medicine, Kurume City, Japan
| | - Toshi Abe
- Department of Radiology, Kurume University School of Medicine, Kurume City, Japan
| | - Naohisa Uchimura
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
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27
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Kim JA, Davis KD. Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences. J Neurophysiol 2021; 125:938-956. [PMID: 33567968 DOI: 10.1152/jn.00530.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.
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Affiliation(s)
- Junseok A Kim
- Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Karen D Davis
- Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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28
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Prefrontal resting-state connectivity and antidepressant response: no associations in the ELECT-TDCS trial. Eur Arch Psychiatry Clin Neurosci 2021; 271:123-134. [PMID: 32880057 DOI: 10.1007/s00406-020-01187-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/20/2020] [Indexed: 12/24/2022]
Abstract
Functional and structural MRI of prefrontal cortex (PFC) may provide putative biomarkers for predicting the treatment response to transcranial direct current stimulation (tDCS) in depression. A recent MRI study from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Theror Depression Study) showed that depression improvement after tDCS was associated with gray matter volumes of PFC subregions. Based thereon, we investigated whether antidepressant effects of tDCS are similarly associated with baseline resting-state functional connectivity (rsFC). A subgroup of 51 patients underwent baseline rsFC-MRI. All patients of ELECT-TDCS were randomized to three treatment arms for 10 weeks (anodal-left, cathodal-right PFC tDCS plus placebo medication; escitalopram 10 mg/day for 3 weeks and 20 mg/day thereafter plus sham tDCS; and placebo medication plus sham tDCS). RsFC was calculated for various PFC regions and analyzed in relation to the individual antidepressant response. There was no significant association between baseline PFC connectivity of essential structural regions, nor any other PFC regions (after correction for multiple comparisons) and patients' individual antidepressant response. This study did not reveal an association between antidepressants effects of tDCS and baseline rsFC, unlike the gray matter volume findings. Thus, the antidepressant effects of tDCS may be differentially related to structural and functional MRI measurements.
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29
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Chahal R, Weissman DG, Marek S, Rhoads SA, Hipwell AE, Forbes EE, Keenan K, Guyer AE. Girls' brain structural connectivity in late adolescence relates to history of depression symptoms. J Child Psychol Psychiatry 2020; 61:1224-1233. [PMID: 31879977 PMCID: PMC7316589 DOI: 10.1111/jcpp.13184] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/20/2019] [Accepted: 11/22/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Girls' depressive symptoms typically increase in adolescence, with individual differences in course and severity being key risk factors for impaired emotional functioning in young adulthood. Given the continued brain white matter (WM) maturation that occurs in adolescence, the present study tested whether structural connectivity patterns in late adolescence are associated with variation in the course of depression symptom severity throughout adolescence. METHOD Participants were girls (N = 115) enrolled in a multiyear prospective cohort study of risk for depression. Initial depression severity (intercept) at age 10 and change in severity (linear slope) across ages 10-19 were examined in relation to WM tractography collected at age 19. Network-based statistic analyses were used to identify clusters showing variation in structural connectivity in association with depressive symptom intercept, slope, and their interaction. RESULTS Higher initial depressive severity and steeper positive slope (separately) were associated with greater structural connectivity between temporal, subcortical socioaffective, and occipital regions. Intercept showed more connectivity associations than slope. The interaction effect indicated that higher initial symptom severity and a steeper negative slope (i.e., alleviating symptoms) were related to greater connectivity between cognitive control regions. Moderately severe symptoms that worsened over time were followed by greater connectivity between self-referential and cognitive regions (e.g., posterior cingulate and frontal gyrus). CONCLUSIONS Higher depressive symptom severity in early adolescence and increasing symptom severity over time may forecast structural connectivity differences in late adolescence, particularly in pathways involving cognitive and emotion-processing regions. Understanding how clinical course relates to neurobiological correlates may inform new treatment approaches to adolescent depression.
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Affiliation(s)
- Rajpreet Chahal
- Department of Human Ecology, University of California, Davis, Davis, CA 95616
- Center for Mind and Brain, University of California, Davis, Davis, CA 95618
| | | | - Scott Marek
- Department of Psychiatry, Washington University, St. Louis, MO 63110
| | - Shawn A. Rhoads
- Department of Psychology, Georgetown University, Washington, DC 20057
| | - Alison E. Hipwell
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213
| | - Erika E. Forbes
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213
| | - Kate Keenan
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637
| | - Amanda E. Guyer
- Department of Human Ecology, University of California, Davis, Davis, CA 95616
- Center for Mind and Brain, University of California, Davis, Davis, CA 95618
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30
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Hill AT, Hadas I, Zomorrodi R, Voineskos D, Farzan F, Fitzgerald PB, Blumberger DM, Daskalakis ZJ. Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis. Sci Rep 2020; 10:17057. [PMID: 33051528 PMCID: PMC7555809 DOI: 10.1038/s41598-020-74103-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/12/2020] [Indexed: 12/18/2022] Open
Abstract
Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient’s final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies.
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Affiliation(s)
- Aron T Hill
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Itay Hadas
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Daphne Voineskos
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Centre for Engineering-Led Brain Research, Simon Fraser University, Surrey, BC, Canada
| | - Paul B Fitzgerald
- Epworth Centre for Innovation in Mental Health, Epworth Healthcare and Monash Alfred Psychiatry Research Centre, The Alfred and Monash University Central Clinical School, Commercial Rd, Melbourne, VIC, Australia
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada. .,Institute of Medical Science, University of Toronto, Toronto, ON, Canada. .,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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31
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Alonso Martínez S, Deco G, Ter Horst GJ, Cabral J. The Dynamics of Functional Brain Networks Associated With Depressive Symptoms in a Nonclinical Sample. Front Neural Circuits 2020; 14:570583. [PMID: 33071760 PMCID: PMC7530893 DOI: 10.3389/fncir.2020.570583] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 08/26/2020] [Indexed: 12/17/2022] Open
Abstract
Brain function depends on the flexible and dynamic coordination of functional subsystems within distributed neural networks operating on multiple scales. Recent progress has been made in the characterization of functional connectivity (FC) at the whole-brain scale from a dynamic, rather than static, perspective, but its validity for cognitive sciences remains under debate. Here, we analyzed brain activity recorded with functional Magnetic Resonance Imaging from 71 healthy participants evaluated for depressive symptoms after a relationship breakup based on the conventional Major Depression Inventory (MDI). We compared both static and dynamic FC patterns between participants reporting high and low depressive symptoms. Between-group differences in static FC were estimated using a standard pipeline for network-based statistic (NBS). Additionally, FC was analyzed from a dynamic perspective by characterizing the occupancy, lifetime, and transition profiles of recurrent FC patterns. Recurrent FC patterns were defined by clustering the BOLD phase-locking patterns obtained using leading eigenvector dynamics analysis (LEiDA). NBS analysis revealed a brain subsystem exhibiting significantly lower within-subsystem correlation values in more depressed participants (high MDI). This subsystem predominantly comprised connections between regions of the default mode network (i.e., precuneus) and regions outside this network. On the other hand, LEiDA results showed that high MDI participants engaged more in a state connecting regions of the default mode, memory retrieval, and frontoparietal network (p-FDR = 0.012); and less in a state connecting mostly the visual and dorsal attention systems (p-FDR = 0.004). Although both our analyses on static and dynamic FC implicate the role of the precuneus in depressive symptoms, only including the temporal evolution of BOLD FC helped to disentangle over time the distinct configurations in which this region plays a role. This finding further indicates that a holistic understanding of brain function can only be gleaned if the temporal dynamics of FC is included.
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Affiliation(s)
- Sonsoles Alonso Martínez
- Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Gert J Ter Horst
- Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom.,Center for Music in the Brain, Aarhus University, Aarhus, Denmark.,Life and Health Sciences Research Institute (ICVS), University of Minho, Minho, Portugal
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Moreno-Ortega M, Kangarlu A, Lee S, Perera T, Kangarlu J, Palomo T, Glasser MF, Javitt DC. Parcel-guided rTMS for depression. Transl Psychiatry 2020; 10:283. [PMID: 32788580 PMCID: PMC7423622 DOI: 10.1038/s41398-020-00970-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 07/15/2020] [Accepted: 07/28/2020] [Indexed: 12/26/2022] Open
Abstract
Transcranial magnetic stimulation (TMS) is an approved intervention for treatment-resistant depression (TRD), but current targeting approaches are only partially successful. Our objectives were (1) to examine the feasibility of MRI-guided TMS in the clinical setting using a recently published surface-based, multimodal parcellation in patients with TRD who failed standard TMS (sdTMS); (2) to examine the neurobiological mechanisms and clinical outcomes underlying MRI-guided TMS compared to that of sdTMS. We used parcel-guided TMS (pgTMS) to target the left dorsolateral prefrontal cortex parcel 46. Resting-state functional connectivity (rsfc) was assessed between parcel 46 and predefined nodes within the default mode and visual networks, following both pgTMS and sdTMS. All patients (n = 10) who had previously failed sdTMS responded to pgTMS. Alterations in rsfc between frontal, default mode, and visual networks differed significantly over time between groups. Improvements in symptoms correlated with alterations in rsfc within each treatment group. The outcome of our study supports the feasibility of pgTMS within the clinical setting. Future prospective, double-blind studies of pgTMS vs. sdTMS appear warranted.
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Affiliation(s)
- M. Moreno-Ortega
- Division of Experimental Therapeutics, Department of Psychiatry, New York State Psychiatric Institute/Columbia University Medical Center, New York, NY USA ,grid.469673.9Centro de Investigacion Biomedica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - A. Kangarlu
- grid.21729.3f0000000419368729Department of Psychiatry, Radiology and Biomedical Engineering, Columbia University, New York, NY USA
| | - S. Lee
- grid.21729.3f0000000419368729Department of Psychiatry and Biostatistics, New York State Psychiatric Institute/Columbia University, New York, NY USA
| | - T. Perera
- Contemporary Care, Greenwich, CT USA
| | - J. Kangarlu
- grid.411023.50000 0000 9159 4457State University of New York (SUNY) Upstate Medical University, Syracuse, NY USA
| | - T. Palomo
- grid.469673.9Centro de Investigacion Biomedica en Red de Salud Mental (CIBERSAM), Madrid, Spain ,grid.4795.f0000 0001 2157 7667Department of Psychiatry, Complutense University, Madrid, Spain
| | - M. F. Glasser
- grid.4367.60000 0001 2355 7002Departments of Radiology and Neuroscience, Washington University Medical School, St. Louis, USA
| | - D. C. Javitt
- Division of Experimental Therapeutics, Department of Psychiatry, New York State Psychiatric Institute/Columbia University Medical Center, New York, NY USA
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Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder. Mol Neurobiol 2020; 57:4031-4044. [PMID: 32651757 DOI: 10.1007/s12035-020-01995-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 06/22/2020] [Indexed: 10/23/2022]
Abstract
Major depressive disorder (MDD) has been characterized by abnormal brain activity and interactions across the whole-brain functional networks. However, the underlying alteration of brain dynamics remains unclear. Here, we aim to investigate in detail the temporal dynamics of brain activity for MDD, and to characterize the spatiotemporal specificity of whole-brain networks and transitions across them. We developed a hidden Markov model (HMM) analysis for resting-state functional magnetic resonance imaging (fMRI) from two independent cohorts with MDD. In particular, one cohort included 127 MDD patients and 117 gender- and age-matched healthy controls, and the other included 44 MDD patients and 33 controls. We identified brain states characterized by the engagement of distinct functional networks that recurred over time and assessed the dynamical configuration of whole-brain networks and the patterns of activation of states that characterized the MDD groups. Furthermore, we analyzed the community structure of transitions across states to investigate the specificity and abnormality of transitions for MDD. Based on our identification of 12 HMM states, we found that the temporal reconfiguration of states in MDD was associated with the high-order cognition network (DMN), subcortical network (SUB), and sensory and motor networks (SMN). Further, we found that the specific module of transitions was closely related to MDD, which were characterized by two HMM states with opposite activations in DMN, SMN, and subcortical areas. Notably, our results provide novel insights into the dynamical circuit configuration of whole-brain networks for MDD and suggest that brain dynamics should remain a prime target for further MDD research.
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Luccarelli J, McCoy TH, Seiner SJ, Henry ME. Maintenance ECT is associated with sustained improvement in depression symptoms without adverse cognitive effects in a retrospective cohort of 100 patients each receiving 50 or more ECT treatments. J Affect Disord 2020; 271:109-114. [PMID: 32479305 PMCID: PMC7289157 DOI: 10.1016/j.jad.2020.03.152] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/19/2020] [Accepted: 03/29/2020] [Indexed: 12/28/2022]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is the most effective acute treatment for depression, but relapse is common following discontinuation. One strategy for prolonging remission is the use of maintenance ECT, but the clinical evidence supporting its efficacy and safety are limited. We examined the effects of maintenance ECT on depression and cognition. METHODS Participants were from a retrospective cohort of 100 patients receiving ECT at a freestanding psychiatric hospital and who received at least 50 treatments during a single treatment series. QIDS, BASIS-24, and MoCA were assessed at baseline and every 10 treatments thereafter during the clinical course. RESULTS ECT was associated with a rapid decrease in depression symptoms and overall self-reported mental health status within the first 10 treatments, which was sustained throughout a median of 22.1 months of follow-up. There was no change in cognitive functioning as measured by the MoCA. Bilateral and brief pulse treatment parameters were more common by treatment 50 than at the first treatment. Most participants either continued in ECT at the end of the study period or discontinued due to sustained remission. LIMITATIONS retrospective observational study without control group who did not receive ECT. CONCLUSIONS In this ECT cohort with at least 50 treatments, improvement in depression was sustained on QIDS and BASIS-24 and adverse cognitive effects were not detected by serial MoCAs, supporting the utility of maintenance ECT in this cohort.
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Affiliation(s)
- James Luccarelli
- Department of Psychiatry, Massachusetts General Hospital, Boston (Luccarelli, Henry, McCoy); Department of Psychiatry, McLean Hospital, Belmont (Seiner).
| | | | | | - Michael E. Henry
- Department of Psychiatry, Massachusetts General Hospital, Boston (Luccarelli, Henry, McCoy); Department of Psychiatry, McLean Hospital, Belmont (Seiner)
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Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging. Schizophr Res 2020; 216:262-271. [PMID: 31826827 DOI: 10.1016/j.schres.2019.11.046] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/04/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022]
Abstract
Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.
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Sun H, Jiang R, Qi S, Narr KL, Wade BS, Upston J, Espinoza R, Jones T, Calhoun VD, Abbott CC, Sui J. Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data. NEUROIMAGE-CLINICAL 2019; 26:102080. [PMID: 31735637 PMCID: PMC7229344 DOI: 10.1016/j.nicl.2019.102080] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 11/03/2019] [Accepted: 11/05/2019] [Indexed: 12/12/2022]
Abstract
The negative FC networks achieve predictive accuracy of 76.23% for ECT response. The consensus FCs represent predominately frontal, temporal and subcortical regions. FCs that changed significantly were concentrated in frontal and limbic networks. Longitudinal change overlapped with two FCs compared with FC with predictive power.
Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies.
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Affiliation(s)
- Hailun Sun
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
| | - Benjamin Sc Wade
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
| | - Tom Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | | | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
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Functional connectivity of the anterior cingulate cortex predicts treatment outcome for rTMS in treatment-resistant depression at 3-month follow-up. Brain Stimul 2019; 13:206-214. [PMID: 31668646 DOI: 10.1016/j.brs.2019.10.012] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 10/09/2019] [Accepted: 10/16/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Repetitive transcranial magnetic stimulation (rTMS) is a first-line treatment for treatment-resistant depression (TRD). The mechanisms of action of rTMS are not fully understood, and no biomarkers are available to assist in clinical practice to predict response to rTMS. This study aimed to demonstrate that after-rTMS clinical improvement is associated with functional connectivity (FC) changes of the subgenual cingulate cortex (sgACC) and rostral anterior cingulate (rACC), and FC of sgACC and rACC might serve as potential predictors for treatment response. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected within 1 week before rTMS initiation in 50 TRD patients to predict subsequent response to rTMS on the left dorsolateral prefrontal cortex (DLPFC). Follow-up rs-fMRI was obtained 12 weeks after completion of rTMS and neural correlates of rTMS in sgACC- and rACC-related FC patterns were compared to before rTMS data and with rs-fMRI from healthy participants. RESULTS Treatment response was associated with lower FC of sgACC to right DLPFC and higher FC of rACC to left lateral parietal cortex (IPL) measured at baseline. Using sgACC-DLPFC and rACC-IPL connectivity as features, responder-nonresponder classification accuracies of 84% and 76% (end-of-treatment), 88% and 81% (3-month follow-up), respectively were achieved. Longitudinal rs-fMRI data analyses revealed that the hyperconnectivity between sgACC and visual cortex was normalized to a level which was comparable to that of healthy participants. CONCLUSIONS Brain activity patterns in depression are predictive of treatment response to rTMS, and longitudinal change of brain activity in relevant brain circuits after rTMS is associated with treatment response in depression. Target engagement paradigms may offer opportunities to increase the efficacy of rTMS in TRD by optimal selection of patients for treatment. TRIAL REGISTRATION ClinicalTrials.gov Identifiers: NCT01887782 and NCT02800226.
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