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Dam S, Batail JM, Robert GH, Drapier D, Maurel P, Coloigner J. Structural Brain Connectivity and Treatment Improvement in Mood Disorder. Brain Connect 2024; 14:239-251. [PMID: 38534988 DOI: 10.1089/brain.2023.0063] [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] [Indexed: 04/25/2024] Open
Abstract
Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.
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Affiliation(s)
- Sébastien Dam
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Jean-Marie Batail
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Gabriel H Robert
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Dominique Drapier
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Pierre Maurel
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Julie Coloigner
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
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Tong X, Xie H, Wu W, Keller CJ, Fonzo GA, Chidharom M, Carlisle NB, Etkin A, Zhang Y. Individual deviations from normative electroencephalographic connectivity predict antidepressant response. J Affect Disord 2024; 351:220-230. [PMID: 38281595 PMCID: PMC10923099 DOI: 10.1016/j.jad.2024.01.177] [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: 08/23/2023] [Revised: 01/15/2024] [Accepted: 01/18/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. METHODS We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. RESULTS We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. CONCLUSIONS Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. TRIAL REGISTRATION Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA; George Washington University School of Medicine, Washington, DC, USA
| | - Wei Wu
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA; Veterans Affairs Palo Alto Healthcare System, Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | | | | | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
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Yue M, Peng X, Chunlei G, Yi L, Shanshan G, Jifei S, Qingyan C, Bai Z, Yong L, Zhangjin Z, Peijing R, Jiliang F. Modulating the default mode network: Antidepressant efficacy of transcutaneous electrical cranial-auricular acupoints stimulation targeting the insula. Psychiatry Res Neuroimaging 2024; 339:111787. [PMID: 38295529 DOI: 10.1016/j.pscychresns.2024.111787] [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: 07/31/2023] [Revised: 11/22/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Transcutaneous electrical cranial-auricular acupoint stimulation (TECAS) is a novel non-invasive therapy for major depressive disorder (MDD) that stimulates acupoints innervated by the trigeminal and auricular vagus nerves. However, there are few neuroimaging studies involving the TECAS for the treatment of MDD. Therefore, this study aimed to investigate the treatment response and neurological effects of TECAS using resting-state functional magnetic resonance imaging (rs-fMRI). METHOD A total of 34 patients with mild-to-moderate MDD and 34 demographically matched healthy controls (HCs) were recruited. After an eight-week treatment the primary outcome was clinical response, defined as a baseline-to-endpoint ≥ 50 % reduction in the 17-item Hamilton Depression Rating Scale (HAMD-17). The low-frequency fluctuations (ALFF) method were used to investigate the brain abnormalities of MDD patients and HCs, and altered brain networks were analyzed between pre- and post-treatment using seed-based functional connectivity (FC) analysis. RESULTS We found no significant differences in terms of gender, age, and years of education between the two groups. After treatment, the response rate was 58.82 %. Compared to HCs, MDD patients showed lower ALFF values in the left insula(t = -4.298,P < 0.005), the insula-based FC revealed in the right middle frontal gyrus (MFG)/ right superior frontal gyrus, orbital part (ORBsupmed) (t = -5.29,P < 0.005) and the right anterior cingulate gyrus (ACC)were decreased (t = -6.08,P < 0.005). Furthermore, Compared to pre-treatment, abnormal FC values in the ACC /orbital superior frontal gyrus (SFG) (t = 3.42,P < 0.005) and left superior frontal gyrus (SFG)/ supplement motor area (SMA) were enhanced (t = 3.34,P < 0.005). CONCLUSION TECAS exhibits antidepressant efficacy, particularly influencing the insula-based functional connections within the Default Mode Network (DMN) related to emotion processing in individuals with MDD.
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Affiliation(s)
- Ma Yue
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China; Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China
| | - Xu Peng
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China
| | - Guo Chunlei
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China; Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China
| | - Luo Yi
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China; Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China
| | - Gao Shanshan
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China; Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China
| | - Sun Jifei
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China; Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China
| | - Chen Qingyan
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China; Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China
| | - Zhenjun Bai
- College of Traditional Chinese Medicine Health Service, Shanxi Datong University, Datong, 037009, Shanxi Province, China
| | - Liu Yong
- Affiliated Hospital of Traditional Chinese Medicine, Southwest Medical University, 646000, Luzhou, China
| | - Zhang Zhangjin
- Department of Chinese Medicine, the University of Hong Kong-Shenzhen Hospital (HKU-SZH), Shenzhen, China
| | - Rong Peijing
- Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China; Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, 100700, Beijing, China
| | - Fang Jiliang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, 100053, Beijing, China; Graduate School of China Academy of Chinese Medical Sciences, 100700, Beijing, China.
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Zhang J, Zhong H, Zhang Y, Yin J, Song X, Ye K, Song Z, Lai S, Zhong S, Wang Z, Jia Y. Personality traits as predictors for treatment response to sertraline among unmedicated obsessive-compulsive Disorder: A 12-weeks retrospective longitudinal study. J Psychiatr Res 2024; 170:245-252. [PMID: 38171218 DOI: 10.1016/j.jpsychires.2023.12.021] [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: 05/18/2023] [Revised: 12/02/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024]
Abstract
The effectiveness of selective serotonin reuptake inhibitors (SSRIs) as a primary treatment for obsessive-compulsive disorder (OCD) remains uncertain. Even after undergoing standard SSRIs treatment, 40%-60% of individuals with OCD persistently endure symptoms. Recent studies proposed that personality traits may influence the diversity of OCD treatment results. Thus, in this retrospective study, we evaluated the Eysenck Personality Questionnaire (EPQ) scores of 51 untreated patients with OCD and 35 healthy controls. The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) was employed to assess OCD symptom severity at weeks 0, 2, 4, 8, and 12 of sertraline treatment. The primary outcome focused on the reduction rate of Y-BOCS scores (response: ≥25%; marked response: ≥50%). Our findings revealed that individuals with OCD demonstrated a significantly higher neuroticism score compared to healthy controls. Correlation analyses exposed a positive link between psychoticism and the duration of the disease. Moreover, family history strongly correlated with both obsessive thoughts and the total Y-BOCS score. Subsequent univariate Cox proportional analyses indicated that both low neuroticism and high extraversion traits could forecast the response to sertraline. Furthermore, only a high extraversion trait was linked to a marked response. Our results support the idea that personality traits may contribute to OCD vulnerability and predict sertraline treatment outcomes.
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Affiliation(s)
- Jianzhao Zhang
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Hui Zhong
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China; Department of Child and Adolescents Psychology, Anhui Mental Health Center, Hefei 230022, China
| | - Yiliang Zhang
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China.
| | - Jie Yin
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Xiaodong Song
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Kaiwei Ye
- School of Management, Jinan University, Guangzhou 510630, China
| | - Zijin Song
- School of Management, Jinan University, Guangzhou 510630, China
| | - Shunkai Lai
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Zhen Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China.
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Leonards CA, Harrison BJ, Jamieson AJ, Agathos J, Steward T, Davey CG. Altered task-related decoupling of the rostral anterior cingulate cortex in depression. Neuroimage Clin 2024; 41:103564. [PMID: 38218081 PMCID: PMC10821626 DOI: 10.1016/j.nicl.2024.103564] [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: 10/09/2023] [Revised: 12/08/2023] [Accepted: 01/07/2024] [Indexed: 01/15/2024]
Abstract
Dysfunctional activity of the rostral anterior cingulate cortex (rACC) - an extensively connected hub region of the default mode network - has been broadly linked to cognitive and affective impairments in depression. However, the nature of aberrant task-related rACC suppression in depression is incompletely understood. In this study, we sought to characterize functional connectivity of rACC activity suppression ('deactivation') - an essential feature of rACC function - during external task engagement in depression. Specifically, we aimed to explore neural patterns of functional decoupling and coupling with the rACC during its task-driven suppression. We enrolled 81 15- to 25-year-old young people with moderate-to-severe major depressive disorder (MDD) before they commenced a 12-week clinical trial that assessed the effectiveness of cognitive behavioral therapy plus either fluoxetine or placebo. Ninety-four matched healthy controls were also recruited. Participants completed a functional magnetic resonance imaging face matching task known to elicit rACC suppression. To identify brain regions associated with the rACC during its task-driven suppression, we employed a seed-based functional connectivity analysis. We found MDD participants, compared to controls, showed significantly reduced 'decoupling' of the rACC with extended task-specific regions during task performance. Specifically, less decoupling was observed in the occipital and fusiform gyrus, dorsal ACC, medial prefrontal cortex, cuneus, amygdala, thalamus, and hippocampus. Notably, impaired decoupling was apparent in participants who did not remit to treatment, but not treatment remitters. Further, we found MDD participants showed significant increased coupling with the anterior insula cortex during task engagement. Our findings indicate that aberrant task-related rACC suppression is associated with disruptions in adaptive neural communication and dynamic switching between internal and external cognitive modes that may underpin maladaptive cognitions and biased emotional processing in depression.
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Affiliation(s)
- Christine A Leonards
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - Ben J Harrison
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - Alec J Jamieson
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - James Agathos
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - Trevor Steward
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Christopher G Davey
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia.
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Berkovitch L, Lee K, Ji JL, Helmer M, Rahmati M, Demšar J, Kraljič A, Matkovič A, Tamayo Z, Murray JD, Repovš G, Krystal JH, Martin WJ, Fonteneau C, Anticevic A. A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.15.23300019. [PMID: 38168378 PMCID: PMC10760263 DOI: 10.1101/2023.12.15.23300019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Importance Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design Double blind randomized controlled trial. Setting Multicenter. Participants Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Interventions Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2). Main Outcomes and Measures To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group. Results 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group. Conclusions and Relevance Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.
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Affiliation(s)
- Lucie Berkovitch
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Université Paris Cité, Paris, France
- Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
- Unicog, Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France
| | - Kangjoo Lee
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Jie Lisa Ji
- Manifest Technologies, Inc. New Haven, CT, USA
| | | | | | - Jure Demšar
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Aleksij Kraljič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Zailyn Tamayo
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - John D Murray
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John H Krystal
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Clara Fonteneau
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Alan Anticevic
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Psychology, Yale University School of Medicine, New Haven, CT, USA
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Cormie MA, Kaya B, Hadjis GE, Mouseli P, Moayedi M. Insula-cingulate structural and functional connectivity: an ultra-high field MRI study. Cereb Cortex 2023; 33:9787-9801. [PMID: 37429832 PMCID: PMC10656949 DOI: 10.1093/cercor/bhad244] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 07/12/2023] Open
Abstract
The insula and the cingulate are key brain regions with many heterogenous functions. Both regions are consistently shown to play integral roles in the processing of affective, cognitive, and interoceptive stimuli. The anterior insula (aINS) and the anterior mid-cingulate cortex (aMCC) are two key hubs of the salience network (SN). Beyond the aINS and aMCC, previous 3 Tesla (T) magnetic resonance imaging studies have suggested both structural connectivity (SC) and functional connectivity (FC) between other insular and cingulate subregions. Here, we investigate the SC and FC between insula and cingulate subregions using ultra-high field 7T diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI). DTI revealed strong SC between posterior INS (pINS) and posterior MCC (pMCC), and rs-fMRI revealed strong FC between the aINS and aMCC that was not supported by SC, indicating the likelihood of a mediating structure. Finally, the insular pole had the strongest SC to all cingulate subregions, with a slight preference for the pMCC, indicative of a potential relay node of the insula. Together these finding shed new light on the understanding of insula-cingulate functioning, both within the SN and other cortical processes, through a lens of its SC and FC.
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Affiliation(s)
- Matthew A Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Batu Kaya
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Georgia E Hadjis
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Pedram Mouseli
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Dentistry, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
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8
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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9
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Zhao K, Xie H, Fonzo GA, Tong X, Carlisle N, Chidharom M, Etkin A, Zhang Y. Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression. Mol Psychiatry 2023; 28:2490-2499. [PMID: 36732585 DOI: 10.1038/s41380-023-01958-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/04/2023] [Accepted: 01/11/2023] [Indexed: 02/04/2023]
Abstract
Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | | | - Amit Etkin
- Alto Neuroscience, Inc, Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
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10
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Tong X, Xie H, Wu W, Keller C, Fonzo G, Chidharom M, Carlisle N, Etkin A, Zhang Y. Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.24.23290434. [PMID: 37292874 PMCID: PMC10246152 DOI: 10.1101/2023.05.24.23290434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
- George Washington University School of Medicine, Washington, DC, USA
| | - Wei Wu
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Corey Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA
| | - Gregory Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | | | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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11
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Eldaief MC, McMains S, Izquierdo-Garcia D, Daneshzand M, Nummenmaa A, Braga RM. Network-specific metabolic and haemodynamic effects elicited by non-invasive brain stimulation. NATURE MENTAL HEALTH 2023; 1:346-360. [PMID: 37982031 PMCID: PMC10655825 DOI: 10.1038/s44220-023-00046-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 03/06/2023] [Indexed: 11/21/2023]
Abstract
Repetitive transcranial magnetic stimulation (TMS), when applied to the dorsolateral prefrontal cortex (dlPFC), treats depression. Therapeutic effects are hypothesized to arise from propagation of local dlPFC stimulation effects across distributed networks; however, the mechanisms of this remain unresolved. dlPFC contains representations of different networks. As such, dlPFC TMS may exert different effects depending on the network being stimulated. Here, to test this, we applied high-frequency TMS to two nearby dlPFC targets functionally embedded in distinct anti-correlated networks-the default and salience networks- in the same individuals in separate sessions. Local and distributed TMS effects were measured with combined 18fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging. Identical TMS patterns caused opposing effects on local glucose metabolism: metabolism increased at the salience target following salience TMS but decreased at the default target following default TMS. At the distributed level, both conditions increased functional connectivity between the default and salience networks, with this effect being dramatically larger following default TMS. Metabolic and haemodynamic effects were also linked: across subjects, the magnitude of local metabolic changes correlated with the degree of functional connectivity changes. These results suggest that TMS effects upon dlPFC are network specific. They also invoke putative antidepressant mechanisms of TMS: network de-coupling.
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Affiliation(s)
- Mark C. Eldaief
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Science, Neuroimaging Facility, Harvard University, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - David Izquierdo-Garcia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Mohammad Daneshzand
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Rodrigo M. Braga
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
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12
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Experiencing sweet taste is associated with an increase in prosocial behavior. Sci Rep 2023; 13:1954. [PMID: 36732349 PMCID: PMC9894851 DOI: 10.1038/s41598-023-28553-9] [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: 09/19/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Taste may be the first sense that emerged in evolution. Taste is also a very important sense since it signals potential beneficial or dangerous effects of foods. Given this fundamental role of taste in our lives, it is not surprising that taste also affects our psychological perception and thinking. For example, previous research demonstrated remarkable psychological effects of sweet taste experiences, suggesting that sweetness may be a source domain for prosocial functioning. Recent research reports that briefly experiencing sweet taste made participants more helpful in their intentions and behavior. The current study aims to test this hypothesis and to examine the neural underpinnings of this effect by using an fMRI approach. Participants were asked to taste sweet, salty, and neutral taste while lying in the fMRI scanner. Subsequently their prosocial behavior was tested by playing the dictator game, a measure of prosocial behavior. Results showed that sweet taste was associated with an increase in prosocial behavior compared with previously experiencing salty taste but did not affect control stimuli ratings. FMRI results revealed a modulation of the dorsal anterior cingulate cortex associated with this sweetness effect. This brain area is known to play a central role for monitoring conflicts and decisions and has been directly linked to selfish and prosocial economic decisions. The results demonstrate that sweet taste has complex psychological effects including positive and socially desirable outcomes. We discuss the results with other studies on psychological sweetness effects and suggest possible implications of these findings.
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13
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Zhang S, Liu L, Zhang L, Ma L, Wu H, He X, Cao M, Li R. Evaluating the treatment outcomes of repetitive transcranial magnetic stimulation in patients with moderate-to-severe Alzheimer's disease. Front Aging Neurosci 2023; 14:1070535. [PMID: 36688172 PMCID: PMC9853407 DOI: 10.3389/fnagi.2022.1070535] [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: 10/15/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
The repetitive transcranial magnetic stimulation (rTMS) shows great potential in the treatment of Alzheimer's disease (AD). However, its treatment efficacy for AD patients in moderate to severe stage is relatively evaluated. Here, we proposed a randomized, sham-controlled, clinical trial of rTMS among 35 moderate-to-severe AD patients. A high frequency (10 Hz) stimulation of the left dorsal lateral prefrontal cortex (DLPFC), 60-session long treatment lasting for 3 months procedure was adopted in the trial. Each participant completed a battery of neuropsychological tests at baseline and post-treatment for evaluation of the rTMS therapeutic effect. Twelve of them completed baseline resting-state functional magnetic resonance imaging (fMRI) for exploration of the underlying neural contribution to individual difference in treatment outcomes. The result showed that the rTMS treatment significantly improved cognitive performance on the severe impairment battery (SIB), reduced psychiatric symptoms on the neuropsychiatric inventory (NPI), and improved the clinician's global impression of change (CIBIC-Plus). Furthermore, the result preliminarily proposed resting-state multivariate functional connectivity in the (para) hippocampal region as well as two clusters in the frontal and occipital cortices as a pre-treatment neuroimaging marker for predicting individual differences in treatment outcomes. The finding could brought some enlightenment and reference for the rTMS treatment of moderate and severe AD patients.
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Affiliation(s)
- Shouzi Zhang
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China,*Correspondence: Shouzi Zhang, ✉
| | - Lixin Liu
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Li Zhang
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Li Ma
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Haiyan Wu
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Xuelin He
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Meng Cao
- Department of Psychiatry, Beijing Geriatric Hospital, Beijing, China
| | - Rui Li
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,Rui Li, ✉
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14
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Michelini G, Norman LJ, Shaw P, Loo SK. Treatment biomarkers for ADHD: Taking stock and moving forward. Transl Psychiatry 2022; 12:444. [PMID: 36224169 PMCID: PMC9556670 DOI: 10.1038/s41398-022-02207-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Abstract
The development of treatment biomarkers for psychiatric disorders has been challenging, particularly for heterogeneous neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). Promising findings are also rarely translated into clinical practice, especially with regard to treatment decisions and development of novel treatments. Despite this slow progress, the available neuroimaging, electrophysiological (EEG) and genetic literature provides a solid foundation for biomarker discovery. This article gives an updated review of promising treatment biomarkers for ADHD which may enhance personalized medicine and novel treatment development. The available literature points to promising pre-treatment profiles predicting efficacy of various pharmacological and non-pharmacological treatments for ADHD. These candidate predictive biomarkers, particularly those based on low-cost and non-invasive EEG assessments, show promise for the future stratification of patients to specific treatments. Studies with repeated biomarker assessments further show that different treatments produce distinct changes in brain profiles, which track treatment-related clinical improvements. These candidate monitoring/response biomarkers may aid future monitoring of treatment effects and point to mechanistic targets for novel treatments, such as neurotherapies. Nevertheless, existing research does not support any immediate clinical applications of treatment biomarkers for ADHD. Key barriers are the paucity of replications and external validations, the use of small and homogeneous samples of predominantly White children, and practical limitations, including the cost and technical requirements of biomarker assessments and their unknown feasibility and acceptability for people with ADHD. We conclude with a discussion of future directions and methodological changes to promote clinical translation and enhance personalized treatment decisions for diverse groups of individuals with ADHD.
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Affiliation(s)
- Giorgia Michelini
- grid.4868.20000 0001 2171 1133Department of Biological and Experimental Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA USA
| | - Luke J. Norman
- grid.416868.50000 0004 0464 0574Office of the Clinical Director, NIMH, Bethesda, MD USA
| | - Philip Shaw
- grid.416868.50000 0004 0464 0574Office of the Clinical Director, NIMH, Bethesda, MD USA ,grid.280128.10000 0001 2233 9230Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, MD USA
| | - Sandra K. Loo
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA USA
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15
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Smith EE, Bel-Bahar TS, Kayser J. A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA. Psychophysiology 2022; 59:e14080. [PMID: 35478408 PMCID: PMC9427703 DOI: 10.1111/psyp.14080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 04/04/2022] [Accepted: 04/07/2022] [Indexed: 11/27/2022]
Abstract
Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.
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Affiliation(s)
- Ezra E. Smith
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Tarik S. Bel-Bahar
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Jürgen Kayser
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
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16
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Smith EE, Choi KS, Veerakumar A, Obatusin M, Howell B, Smith AH, Tiruvadi V, Crowell AL, Riva-Posse P, Alagapan S, Rozell CJ, Mayberg HS, Waters AC. Time-frequency signatures evoked by single-pulse deep brain stimulation to the subcallosal cingulate. Front Hum Neurosci 2022; 16:939258. [PMID: 36061500 PMCID: PMC9433578 DOI: 10.3389/fnhum.2022.939258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Precision targeting of specific white matter bundles that traverse the subcallosal cingulate (SCC) has been linked to efficacy of deep brain stimulation (DBS) for treatment resistant depression (TRD). Methods to confirm optimal target engagement in this heterogenous region are now critical to establish an objective treatment protocol. As yet unexamined are the time-frequency features of the SCC evoked potential (SCC-EP), including spectral power and phase-clustering. We examined these spectral features—evoked power and phase clustering—in a sample of TRD patients (n = 8) with implanted SCC stimulators. Electroencephalogram (EEG) was recorded during wakeful rest. Location of electrical stimulation in the SCC target region was the experimental manipulation. EEG was analyzed at the surface level with an average reference for a cluster of frontal sensors and at a time window identified by prior study (50–150 ms). Morlet wavelets generated indices of evoked power and inter-trial phase clustering. Enhanced phase clustering at theta frequency (4–7 Hz) was observed in every subject and was significantly correlated with SCC-EP magnitude, but only during left SCC stimulation. Stimulation to dorsal SCC evinced stronger phase clustering than ventral SCC. There was a weak correlation between phase clustering and white matter density. An increase in evoked delta power (2–4 Hz) was also coincident with SCC-EP, but was less consistent across participants. DBS evoked time-frequency features index mm-scale changes to the location of stimulation in the SCC target region and correlate with structural characteristics implicated in treatment optimization. Results also imply a shared generative mechanism (inter-trial phase clustering) between evoked potentials evinced by electrical stimulation and evoked potentials evinced by auditory/visual stimuli and behavioral tasks. Understanding how current injection impacts downstream cortical activity is essential to building new technologies that adapt treatment parameters to individual differences in neurophysiology.
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Affiliation(s)
| | - Ki Sueng Choi
- Departments of Psychiatry, Neuroscience, Neurology, Neurosurgery and Radiology, Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ashan Veerakumar
- Department of Psychiatry, Schulich School of Medicine and Dentistry, London, ON, Canada
| | - Mosadoluwa Obatusin
- Departments of Psychiatry, Neuroscience, Neurology, Neurosurgery and Radiology, Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Bryan Howell
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Andrew H. Smith
- Departments of Psychiatry, Neuroscience, Neurology, Neurosurgery and Radiology, Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Vineet Tiruvadi
- Emory University School of Medicine, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA, United States
| | - Andrea L. Crowell
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Sankaraleengam Alagapan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Christopher J. Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Helen S. Mayberg
- Departments of Psychiatry, Neuroscience, Neurology, Neurosurgery and Radiology, Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Allison C. Waters
- Departments of Psychiatry, Neuroscience, Neurology, Neurosurgery and Radiology, Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- *Correspondence: Allison C. Waters,
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17
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Ma X, Yu W, Yao P, Zhu Y, Dai J, He X, Liu B, Xu C, Shao X, Fang J, Shen Z. Afferent and efferent projections of the rostral anterior cingulate cortex in young and middle-aged mice. Front Aging Neurosci 2022; 14:960868. [PMID: 36062147 PMCID: PMC9428471 DOI: 10.3389/fnagi.2022.960868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/25/2022] [Indexed: 12/03/2022] Open
Abstract
Research shows that across life, the incidence of mental illness is highest in the young. In the context of the COVID-19 pandemic, mental health issues of the young in particular have received global attention. The rostral anterior cingulate cortex (rACC) plays an important role in psychiatric disorders and chronic pain-psychiatric comorbidities. However, it remains unknown whether or how the afferent and efferent circuits of the rACC change with aging. In this study, we microinjected a retrograde tracer virus and an anterograde trans-monosynaptic virus into the rACC of young and middle-aged mice (both male and female), and systematically and quantitatively analyzed the whole-brain afferent and efferent connections of rACC at different ages and sexes. Notably, in young and middle-aged mice, afferents of the rACC belong to four groups of brain structures arising mainly from the amygdala [mainly basolateral amygdaloid nucleus (BLA)] and cerebral cortex (mainly orbital cortex), with a small part originating from the basal forebrain and thalamus. In contrast, efferents of the rACC belong to four groups of brain structures mainly projecting to the thalamus (mainly ventral anterior-lateral/ventromedial thalamic nucleus (VAL/VM)], with a very small part projecting to the amygdala, basal forebrain, and cerebral cortex. Compared with young mice, the BLA-rACC circuit in middle-aged mice (male and female) did not change significantly, while the rACC-VAL/VM circuit in middle-aged mice (male and female) decreased significantly. In conclusion, this study comprehensively analyzed the input-output neural projections of rACC in mice of different ages and sexes and provided preliminary evidence for further targeted research.
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18
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Afzali MH, Dagher A, Bourque J, Spinney S, Conrod P. Cross-lagged Relationships Between Depressive Symptoms and Altered Default Mode Network Connectivity Over the Course of Adolescence. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:774-781. [PMID: 34929346 DOI: 10.1016/j.bpsc.2021.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/05/2021] [Accepted: 10/28/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although the peak onset of depressive symptoms occurs during adolescence, very few studies have directly examined depression-related changes in resting-state (RS) default mode network activity during adolescence, controlling for potential neural markers of risk. METHODS This study used data from a longitudinal adolescent cohort to investigate age-specific, persistent (i.e., lagged), and dynamic associations between RS functional connectivity within the default mode network and depressive symptoms during adolescence using a random intercept cross-lagged panel framework. The Neuroventure sample consisted of 151 adolescents ages 12-14 at study entry without any neurological illness who were assessed three times during a 5-year follow-up with 97% follow-up across the three assessments. Depressive symptoms were measured using the depression subscale of the Brief Symptoms Inventory. RS functional magnetic resonance imaging data were collected using a 3T Siemens Magnetom Trio scanner in a single 6-minute sequence. RESULTS After controlling for relationships between random intercepts, future depression risk was predicted by RS couplings in the perigenual anterior cingulate cortex and anterior dorsomedial prefrontal cortex (β = -0.69, p = .014) and in the left inferior parietal lobule and anterior superior frontal gyrus (β = -0.43, p = .035). Increases in depressive symptoms at previous time points significantly predicted changes in functional connectivity between the posterior cingulate cortex and the precuneus and posterior middle temporal gyrus (β = 0.37, p = .039) and between the dorsal precuneus and posterior middle temporal gyrus (β = 0.47, p = .036). CONCLUSIONS This study was able to disassociate the RS brain markers of depression from those that appear to follow early-onset depression.
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Affiliation(s)
- Mohammad H Afzali
- Department of Psychiatry, University of Montréal, Montreal, Québec, Canada
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Josiane Bourque
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sean Spinney
- Department of Psychiatry, University of Montréal, Montreal, Québec, Canada; Department of Computer Science and Operations Research, University of Montréal, Montreal, Québec, Canada; Mila - Quebec AI Institute, Montreal, Québec, Canada
| | - Patricia Conrod
- Department of Psychiatry, University of Montréal, Montreal, Québec, Canada; Centre Hospitalier Universitaire Sainte-Justine, Research Centre, Montreal, Québec, Canada.
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19
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Kühnel A, Czisch M, Sämann PG, Binder EB, Kroemer NB. Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity. Biol Psychiatry 2022; 92:158-169. [PMID: 35260225 DOI: 10.1016/j.biopsych.2022.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/09/2022] [Accepted: 01/13/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk. METHODS Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines. RESULTS We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pperm < .001) and increases in heart rate (R2 = 0.075, pperm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR2 = 0.075, pperm = .030) but not the presence or absence of a mood and anxiety disorder. CONCLUSIONS Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.
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Affiliation(s)
- Anne Kühnel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Munich, Germany.
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- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
| | - Nils B Kroemer
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
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20
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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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21
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Beckmann FE, Seidenbecher S, Metzger CD, Gescher DM, Carballedo A, Tozzi L, O'Keane V, Frodl T. C-reactive protein is related to a distinct set of alterations in resting-state functional connectivity contributing to a differential pathophysiology of major depressive disorder. Psychiatry Res Neuroimaging 2022; 321:111440. [PMID: 35131572 DOI: 10.1016/j.pscychresns.2022.111440] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 12/10/2021] [Accepted: 01/18/2022] [Indexed: 01/23/2023]
Abstract
BACKGROUND Several studies in major depressive disorder (MDD) have found inflammation, especially C-reactive protein (CRP), to be consistently associated with MDD and network dysfunction. The aim was to investigate whether CRP is linked to a distinct set of resting-state functional connectivity (RSFC) alterations. METHODS For this reason, we investigated the effects of diagnosis and elevated blood plasma CRP levels on the RSFC in 63 participants (40 females, mean age 31.4 years) of which were 27 patients with a primary diagnosis of MDD and 36 healthy control-subjects (HC), utilizing a seed-based approach within five well-established RSFC networks obtained using fMRI. RESULTS Of the ten network pairs examined, five showed increased between-network RSFC-values unambiguously connected either to a diagnosis of MDD or elevated CRP levels. For elevated CRP levels, increased RSFC between DMN and AN was found. Patients showed increased RSFC within DMN areas and between the DMN and ECN and VAN, ECN and AN and AN and DAN. CONCLUSIONS The results of this study show dysregulated neural circuits specifically connected to elevated plasma CRP levels and independent of other alterations of RSFC in MDD. This dysfunction in neural circuits might in turn result in a certain immune-inflammatory subtype of MDD.
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Affiliation(s)
- Fienne-Elisa Beckmann
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany
| | - Stephanie Seidenbecher
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany
| | - Coraline D Metzger
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany
| | - Dorothee M Gescher
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital Aachen, RWTH Aachen, Germany
| | - Angela Carballedo
- Department of Psychiatry and Trinity Institute of Neuroscience, Trinity College Dublin, Ireland
| | - Leonardo Tozzi
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany; Department of Psychiatry and Trinity Institute of Neuroscience, Trinity College Dublin, Ireland; Department of Psychiatry, University of Stanford, USA
| | - Veronica O'Keane
- Department of Psychiatry and Trinity Institute of Neuroscience, Trinity College Dublin, Ireland
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany; Department of Psychiatry and Trinity Institute of Neuroscience, Trinity College Dublin, Ireland; Department of Psychiatry, University of Stanford, USA; Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital Aachen, RWTH Aachen, Germany.
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22
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Zhang Y, Lei L, Liu Z, Gao M, Liu Z, Sun N, Yang C, Zhang A, Wang Y, Zhang K. Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder. Front Psychiatry 2022; 13:827536. [PMID: 35990051 PMCID: PMC9381950 DOI: 10.3389/fpsyt.2022.827536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 06/10/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Due to substantial comorbidities of major depressive disorder (MDD) and anxiety disorder (AN), these two disorders must be distinguished. Accurate identification and diagnosis facilitate effective and prompt treatment. EEG biomarkers are a potential research hotspot for neuropsychiatric diseases. The purpose of this study was to investigate the differences in EEG power spectrum at theta oscillations between patients with MDD and patients with AN. METHODS Spectral analysis was used to study 66 patients with MDD and 43 patients with AN. Participants wore 16-lead EEG caps to measure resting EEG signals. The EEG power spectrum was measured using the fast Fourier transform. Independent samples t-test was used to analyze the EEG power values of the two groups, and p < 0.05 was statistically significant. RESULTS EEG power spectrum of the MDD group significantly differed from the AN group in the theta oscillation on 4-7 Hz at eight electrode points at F3, O2, T3, P3, P4, FP1, FP2, and F8. CONCLUSION Participants with anxiety demonstrated reduced power in the prefrontal cortex, left temporal lobe, and right occipital regions. Confirmed by further studies, theta oscillations could be another biomarker that distinguishes MDD from AN.
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Affiliation(s)
- Yu Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Mental Health, Shanxi Medical University, Taiyuan, China
| | - Lei Lei
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ziwei Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Mental Health, Shanxi Medical University, Taiyuan, China
| | - Mingxue Gao
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Mental Health, Shanxi Medical University, Taiyuan, China
| | - Zhifen Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ning Sun
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Chunxia Yang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Aixia Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yikun Wang
- Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
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23
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Kennedy GJ. Antidepressants May Not Be Enough When Frailty Complicates Depression in Late Life. Am J Geriatr Psychiatry 2021; 29:956-957. [PMID: 33455857 DOI: 10.1016/j.jagp.2021.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 01/05/2021] [Indexed: 11/16/2022]
Affiliation(s)
- Gary J Kennedy
- Division of Geriatric Psychiatry, Department of Psychiatry and Behavioral Science, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY.
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24
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Structural brain measures linked to clinical phenotypes in major depression replicate across clinical centres. Mol Psychiatry 2021; 26:2764-2775. [PMID: 33589737 DOI: 10.1038/s41380-021-01039-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 01/08/2021] [Accepted: 01/25/2021] [Indexed: 01/31/2023]
Abstract
Abnormalities in brain structural measures, such as cortical thickness and subcortical volumes, are observed in patients with major depressive disorder (MDD) who also often show heterogeneous clinical features. This study seeks to identify the multivariate associations between structural phenotypes and specific clinical symptoms, a novel area of investigation. T1-weighted magnetic resonance imaging measures were obtained using 3 T scanners for 178 unmedicated depressed patients at four academic medical centres. Cortical thickness and subcortical volumes were determined for the depressed patients and patients' clinical presentation was characterized by 213 item-level clinical measures, which were grouped into several large, homogeneous categories by K-means clustering. The multivariate correlations between structural and cluster-level clinical-feature measures were examined using canonical correlation analysis (CCA) and confirmed with both 5-fold and leave-one-site-out cross-validation. Four broad types of clinical measures were detected based on clustering: an anxious misery composite (composed of item-level depression, anxiety, anhedonia, neuroticism and suicidality scores); positive personality traits (extraversion, openness, agreeableness and conscientiousness); reported history of physical/emotional trauma; and a reported history of sexual abuse. Responses on the item-level anxious misery measures were negatively associated with cortical thickness/subcortical volumes in the limbic system and frontal lobe; reported childhood history of physical/emotional trauma and sexual abuse measures were negatively correlated with entorhinal thickness and left hippocampal volume, respectively. In contrast, the positive traits measures were positively associated with hippocampal and amygdala volumes and cortical thickness of the highly-connected precuneus and cingulate cortex. Our findings suggest that structural brain measures may reflect neurobiological mechanisms underlying MDD features.
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25
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Peciña M, Dombrovski AY, Price R, Karim HT. Understanding the Neurocomputational Mechanisms of Antidepressant Placebo Effects. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2021; 6:e210001. [PMID: 33732892 PMCID: PMC7963355 DOI: 10.20900/jpbs.20210001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Over the last two decades, neuroscientists have used antidepressant placebo probes to examine the biological mechanisms implicated in antidepressant placebo effects. However, findings from these studies have not yet elucidated a model-based theory that would explain the mechanism through which antidepressant expectancies evolve to induce persistent mood changes. Emerging evidence suggests that antidepressant placebo effects may be informed by models of reinforcement learning (RL). Such that an individual's expectation of improvement is updated with the arrival of new sensory evidence, by incorporating a reward prediction error (RPE), which signals the mismatch between the expected (expected value) and perceived improvement. Consistent with this framework, neuroimaging studies of antidepressant placebo effects have demonstrated placebo-induced μ-opioid activation and increased blood-oxygen-level dependent (BOLD) responses in regions tracking expected values (e.g., ventromedial prefrontal cortex (vmPFC)) and RPEs (e.g., ventral striatum (VS)). In this study, we will demonstrate the causal contribution of reward learning signals (expected values and RPEs) to antidepressant placebo effects by experimentally manipulating expected values using transcranial magnetic stimulation (TMS) targeting the vmPFC and μ-opioid striatal RPE signal using pharmacological approaches. We hypothesized that antidepressant placebo expectancies are represented in the vmPFC (expected value) and updated by means of μ-opioid-modulated striatal learning signal. In a 3 × 3 factorial double-blind design, we will randomize 120 antidepressant-free individuals with depressive symptoms to one of three between-subject opioid conditions: the μ-opioid agonist buprenorphine, the μ-opioid antagonist naltrexone, or an inert pill. Within each arm, individuals will be assigned to receive three within-subject counterbalanced forms of TMS targeting the vmPFC-intermittent Theta Burst Stimulation (TBS) expected to potentiate the vmPFC, continuous TBS expected to de-potentiate the vmPFC, or sham TBS. These experimental manipulations will be used to modulate trial-by-trial reward learning signals and related brain activity during the Antidepressant Placebo functional MRI (fMRI) Task to address the following aims: (1) investigate the relationship between reward learning signals within the vmPFC-VS circuit and antidepressant placebo effects; (2) examine the causal contribution of vmPFC expected value computations to antidepressant placebo effects; and (3) investigate the causal contribution of μ-opioid-modulated striatal RPEs to antidepressant placebo effects. The proposed study will be the first to investigate the causal contribution of μ-opioid-modulated vmPFC-VS learning signals to antidepressant placebo responses, paving the way for developing novel treatments modulating learning processes and objective means of quantifying and potentially reducing placebo effects during drug development. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04276259.
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Affiliation(s)
- Marta Peciña
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | | | - Rebecca Price
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Helmet T. Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
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26
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Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:535-563. [PMID: 33834417 DOI: 10.1007/978-981-33-6044-0_27] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.
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27
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Lei L, Zhang Y, Song X, Liu P, Wen Y, Zhang A, Yang C, Sun N, Liu Z, Zhang K. Face Recognition Brain Functional Connectivity in Patients With Major Depression: A Brain Source Localization Study by ERP. Front Psychiatry 2021; 12:662502. [PMID: 34803748 PMCID: PMC8604097 DOI: 10.3389/fpsyt.2021.662502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 09/30/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Patients with major depressive disorder (MDD) presents with face recognition defects. These defects negatively affect their social interactions. However, the cause of these defects is not clear. This study sought to explore whether MDD patients develop facial perceptual processing disorders with characteristics of brain functional connectivity (FC). Methods: Event-related potential (ERP) was used to explore differences between 20 MDD patients and 20 healthy participants with face and non-face recognition tasks based on 64 EEG parameters. After pre-processing of EEG data and source reconstruction using the minimum-norm estimate (MNE), data were converted to AAL90 template to obtain a time series of 90 brain regions. EEG power spectra were determined using Fieldtrip incorporating a Fast Fourier transform. FC was determined for all pairs of brain signals for theta band using debiased estimate of weighted phase-lag index (wPLI) in Fieldtrip. To explore group differences in wPLI, independent t-tests were performed with p < 0.05 to indicate statistical significance. False discovery rate (FDR) correction was used to adjust p-values. Results: The findings showed that amplitude induction by face pictures was higher compared with that of non-face pictures both in MDD and healthy control (HC) groups. Face recognition amplitude in MDD group was lower compared with that in the HC group. Two time periods with significant differences were then selected for further analysis. Analysis showed that FC was stronger in the MDD group compared with that in the HC group in most brain regions in both periods. However, only one FC between two brain regions in HC group was stronger compared with that in the MDD group. Conclusion: Dysfunction in brain FC among MDD patients is a relatively complex phenomenon, exhibiting stronger and multiple connectivity with several brain regions of emotions. The findings of the current study indicate that the brain FC of MDD patients is more complex and less efficient in the initial stage of face recognition.
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Affiliation(s)
- Lei Lei
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Yu Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Xiaotong Song
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Penghong Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Yujiao Wen
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Aixia Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Chunxia Yang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Ning Sun
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Zhifen Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
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28
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Kennedy GJ. The Convergence of Biomedical and Psychosocial Approaches to Neural Network Connectivity in Depression. Am J Geriatr Psychiatry 2020; 28:869-871. [PMID: 32473874 DOI: 10.1016/j.jagp.2020.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 05/01/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Gary J Kennedy
- Division of Geriatric Psychiatry, Department of Psychiatry and Behavioral Science, Montefiore Medical Center, Albert Einstein College of Medicine, 111 East 210th Street, Bronx NY 10467.
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29
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Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report. Neuropsychopharmacology 2020; 45:1390-1397. [PMID: 32349119 PMCID: PMC7297974 DOI: 10.1038/s41386-020-0688-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Anhedonia is thought to reflect deficits in reward processing that are associated with abnormal activity in mesocorticolimbic brain regions. It is expressed clinically as a deficit in the interest or pleasure in daily activities. More severe anhedonia in major depressive disorder (MDD) is a negative predictor of antidepressant response. It is unknown, however, whether the pathophysiology of anhedonia represents a viable avenue for identifying biological markers of antidepressant treatment response. Therefore, this study aimed to examine the relationships between reward processing and response to antidepressant treatment using clinical, behavioral, and functional neuroimaging measures. Eighty-seven participants in the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) protocol received 8 weeks of open-label escitalopram. Clinical correlates of reward processing were assessed at baseline using validated scales to measure anhedonia, and a monetary incentive delay (MID) task during functional neuroimaging was completed at baseline and after 2 weeks of treatment. Response to escitalopram was associated with significantly lower self-reported deficits in reward processing at baseline. Activity during the reward anticipation, but not the reward consumption, phase of the MID task was correlated with clinical response to escitalopram at week 8. Early (baseline to week 2) increases in frontostriatal connectivity during reward anticipation significantly correlated with reduction in depressive symptoms after 8 weeks of treatment. Escitalopram response is associated with clinical and neuroimaging correlates of reward processing. These results represent an important contribution towards identifying and integrating biological, behavioral, and clinical correlates of treatment response. ClinicalTrials.gov: NCT01655706.
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30
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Wu Y, Wu X, Wei Q, Wang K, Tian Y. Differences in Cerebral Structure Associated With Depressive Symptoms in the Elderly With Alzheimer's Disease. Front Aging Neurosci 2020; 12:107. [PMID: 32477094 PMCID: PMC7236549 DOI: 10.3389/fnagi.2020.00107] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 03/30/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Alzheimer's disease (AD) is characterized by global deterioration in multiple cognitive domains. In addition to cognitive impairment, depressive symptoms are common issues that trouble AD patients. The neuroanatomical basis of depressive symptoms in AD patients has yet to be elucidated. Method: Twenty AD patients and 22 healthy controls (HCs) were recruited for the present study. Depressive symptoms in AD patients and HCs were assessed according to the Hamilton Depression Rating Scale (HDRS). Anatomical structural differences were assessed between AD patients and HCs using voxel-based morphometry (VBM) and surface-based morphometry (SBM). Correlation analyses were conducted to investigate relationships between depressive symptoms and structural altered regions. Multiple pattern analysis using linear support vector machine (SVM) was performed in another independent cohort, which was collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) data and contained 20 AD patients and 20 HCs, to distinguish AD patients from HCs. Results: Compared with HCs, AD patients exhibited global cerebral atrophy in gray matter volume (GMV) and cortical thickness, including frontal, parietal, temporal, occipital, and insular lobes. In addition, insular GMV was negatively correlated with depressive symptoms. Moreover, SVM-based classification achieved an accuracy of 77.5%, a sensitivity of 70%, and a specificity of 85% by leave-one-out cross-validation. Conclusion: GMV of the insula displayed atrophy among AD patients, which is associated with depressive symptoms. Our observations provide a potential neural substrate for analysis to examine the co-occurrence of AD with depressive symptoms.
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Affiliation(s)
- Yue Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Qiang Wei
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
- Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
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Pantazatos SP, Yttredahl A, Rubin-Falcone H, Kishon R, Oquendo MA, John Mann J, Miller JM. Depression-related anterior cingulate prefrontal resting state connectivity normalizes following cognitive behavioral therapy. Eur Psychiatry 2020; 63:e37. [PMID: 32284075 PMCID: PMC7355178 DOI: 10.1192/j.eurpsy.2020.34] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Background. Aberrant activity of the subcallosal cingulate (SCC) is a common theme across pharmacologic treatment efficacy prediction studies. The functioning of the SCC in psychotherapeutic interventions is relatively understudied, as are functional differences among SCC subdivisions. We conducted functional connectivity analyses (rsFC) on resting-state functional magnetic resonance imaging (fMRI) data, collected before and after a course of cognitive behavioral therapy (CBT) in patients with major depressive disorder (MDD), using seeds from three SCC subdivisions. Methods. Resting-state data were collected from unmedicated patients with current MDD (Hamilton Depression Rating Scale-17 > 16) before and after 14-sessions of CBT monotherapy. Treatment outcome was assessed using the Beck Depression Inventory (BDI). Rostral anterior cingulate (rACC), anterior subcallosal cingulate (aSCC), and Brodmann’s area 25 (BA25) masks were used as seeds in connectivity analyses that assessed baseline rsFC and symptom severity, changes in connectivity related to symptom improvement after CBT, and prediction of treatment outcomes using whole-brain baseline connectivity. Results. Pretreatment BDI negatively correlated with pretreatment rACC ~ dorsolateral prefrontal cortex and aSCC ~ lateral prefrontal cortex rsFC. In a region-of-interest longitudinal analysis, rsFC between these regions increased post-treatment (p < 0.05FDR). In whole-brain analyses, BA25 ~ paracentral lobule and rACC ~ paracentral lobule connectivities decreased post-treatment. Whole-brain baseline rsFC with SCC did not predict clinical improvement. Conclusions. rsFC features of rACC and aSCC, but not BA25, correlated inversely with baseline depression severity, and increased following CBT. Subdivisions of SCC involved in top-down emotion regulation may be more involved in cognitive interventions, while BA25 may be more informative for interventions targeting bottom-up processing. Results emphasize the importance of subdividing the SCC in connectivity analyses.
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Affiliation(s)
- Spiro P Pantazatos
- Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Ashley Yttredahl
- Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Harry Rubin-Falcone
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.,Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
| | - Ronit Kishon
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Maria A Oquendo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - J John Mann
- Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Jeffrey M Miller
- Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
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Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future. Curr Behav Neurosci Rep 2020. [DOI: 10.1007/s40473-020-00198-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Abstract
Purpose of Review
This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression.
Recent Findings
Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures.
Summary
Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.
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Sunaga M, Takei Y, Kato Y, Tagawa M, Suto T, Hironaga N, Ohki T, Takahashi Y, Fujihara K, Sakurai N, Ujita K, Tsushima Y, Fukuda M. Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study. Front Psychiatry 2020; 11:597. [PMID: 32670117 PMCID: PMC7330711 DOI: 10.3389/fpsyt.2020.00597] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/09/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Bipolar disorder (BD) is a serious psychiatric disorder that is associated with a high suicide rate, and for which no clinical biomarker has yet been identified. To address this issue, we investigated the use of magnetoencephalography (MEG) as a new prospective tool. MEG has been used to evaluate frequency-specific connectivity between brain regions; however, no previous study has investigated the frequency-specific resting-state connectome in patients with BD. This resting-state MEG study explored the oscillatory representations of clinical symptoms of BD via graph analysis. METHODS In this prospective case-control study, 17 patients with BD and 22 healthy controls (HCs) underwent resting-state MEG and evaluations for depressive and manic symptoms. After estimating the source current distribution, orthogonalized envelope correlations between multiple brain regions were evaluated for each frequency band. We separated regions-of-interest into seven left and right network modules, including the frontoparietal network (FPN), limbic network (LM), salience network (SAL), and default mode network (DMN), to compare the intra- and inter-community edges between the two groups. RESULTS In the BD group, we found significantly increased inter-community edges of the right LM-right DMN at the gamma band, and decreased inter-community edges of the right SAL-right FPN at the delta band and the left SAL-right SAL at the theta band. Intra-community edges in the left LM at the high beta band were significantly higher in the BD group than in the HC group. The number of connections in the left LM at the high beta band showed positive correlations with the subjective and objective depressive symptoms in the BD group. CONCLUSION We introduced graph theory into resting-state MEG studies to investigate the functional connectivity in patients with BD. To the best of our knowledge, this is a novel approach that may be beneficial in the diagnosis of BD. This study describes the spontaneous oscillatory brain networks that compensate for the time-domain issues associated with functional magnetic resonance imaging. These findings suggest that the connectivity of the LM at the beta band may be a good objective biological biomarker of the depressive symptoms associated with BD.
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Affiliation(s)
- Masakazu Sunaga
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yutaka Kato
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan.,Tsutsuji Mental Hospital, Tatebayashi, Japan
| | - Minami Tagawa
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan.,Gunma Prefectural Psychiatric Medical Center, Isesaki, Japan
| | - Tomohiro Suto
- Gunma Prefectural Psychiatric Medical Center, Isesaki, Japan
| | - Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Takefumi Ohki
- Department of Neurosurgery, Osaka University Medical School, Suita, Japan
| | - Yumiko Takahashi
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kazuyuki Fujihara
- Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Noriko Sakurai
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Koichi Ujita
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yoshito Tsushima
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Masato Fukuda
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
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Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder. Mol Psychiatry 2020; 25:1537-1549. [PMID: 31695168 PMCID: PMC7303006 DOI: 10.1038/s41380-019-0574-2] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 10/07/2019] [Accepted: 10/23/2019] [Indexed: 12/28/2022]
Abstract
Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.
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35
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Okamoto N, Watanabe K, Ngyuyen L, Ikenouchi A, Kishi T, Iwata N, Kakeda S, Korogi Y, Yoshimura R. Association of Serum Kynurenine Levels and Neural Networks in Patients with First-Episode, Drug-Naïve Major Depression: A Source-Based Morphometry Study. Neuropsychiatr Dis Treat 2020; 16:2569-2577. [PMID: 33154644 PMCID: PMC7605945 DOI: 10.2147/ndt.s279622] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/13/2020] [Indexed: 12/28/2022] Open
Abstract
PURPOSE The kynurenine (KYN) pathway can directly or indirectly influence cerebral volume and neural integrity in patients with major depression (MD). The aim of the present study was to investigate neural network systems and the KYN pathway in patients with first-episode, drug-naïve MD. PATIENTS AND METHODS Twenty right-handed drug-naïve patients, with MD diagnosed using the Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition, Text Revision, Research Version, were included in this study. Magnetic resonance imaging scans and scores on the Hamilton Rating Scale for Depression were assessed, and serum sampling was performed prior to the start of pharmacological treatment. Image processing and data analysis were performed according to our recently published procedure. Serum metabolomes were measured in the cation and anion modes of CE-FTMS-based metabolome analysis. RESULTS We found that serum KYN levels were positively correlated with the Z-scores of the salience network but not with those of the central executive network or default mode network. No associations were observed between serum glutamate levels and the Z-score of any of the three networks. CONCLUSION Our results indicate that serum KYN levels might affect the activity of the salience network in first-episode, drug-naïve patients with MD.
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Affiliation(s)
- Naomichi Okamoto
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Keita Watanabe
- Open Innovation Institute, Kyoto University, Kyoto, Japan
| | - LeHoa Ngyuyen
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.,School of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Atsuko Ikenouchi
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Taro Kishi
- Department of Psychiatry, Fujita Medical University, Toyoake, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Medical University, Toyoake, Japan
| | - Shingo Kakeda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan
| | - Yukunori Korogi
- Department of Radiology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
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Default Mode Network structural alterations in Kocher-Monro trajectory white matter transection: A 3 and 7 tesla simulation modeling approach. PLoS One 2019; 14:e0224598. [PMID: 31697747 PMCID: PMC6837312 DOI: 10.1371/journal.pone.0224598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 10/17/2019] [Indexed: 11/19/2022] Open
Abstract
The Kocher-Monro trajectory to the cerebral ventricular system represents one of the most common surgical procedures in the field of neurosurgery. Several studies have analyzed the specific white matter disruption produced during this intervention, which has no reported adverse neurological outcomes. In this study, a graph-theoretical approach was applied to quantify the structural alterations in whole-brain level connectivity. To this end, 132 subjects were randomly selected from the Human Connectome Project dataset and used to create 3 independent 44 subjects groups. Two of the groups underwent a simulated left/right Kocher-Monro trajectory and the third was kept as a control group. For the right Kocher-Monro approach, the nodal analysis revealed decreased strength in the anterior cingulate gyrus of the transected hemisphere. The network-based statistic analysis revealed a set of right lateralized subnetworks with decreased connectivity strength that is consistent with a subset of the Default Mode Network, Salience Network, and Cingulo-Opercular Network. These findings could allow for a better understanding of structural alterations caused by Kocher-Monro approaches that could reveal previously undetected clinical alterations and inform the process of designing safer and less invasive cerebral ventricular approaches.
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37
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Wu GR, Wang X, Baeken C. Baseline functional connectivity may predict placebo responses to accelerated rTMS treatment in major depression. Hum Brain Mapp 2019; 41:632-639. [PMID: 31633261 PMCID: PMC7267925 DOI: 10.1002/hbm.24828] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 10/03/2019] [Indexed: 01/04/2023] Open
Abstract
Although in theory sham repetitive transcranial magnetic stimulation (rTMS) has no inherent therapeutic value, nonetheless, such placebo stimulations may have relevant therapeutic effects in clinically depressed patients. On the other hand, antidepressant responses to sham rTMS are quite heterogeneous across individuals and its neural underpinnings have not been explored yet. The current brain imaging study aims to detect baseline neural fingerprints resulting in clinically beneficial placebo rTMS treatment responses. We collected resting‐state functional magnetic resonance imaging data prior to a registered randomized clinical trial of accelerated placebo stimulation protocol in patients documented with treatment‐resistant depression (http://clinicaltrials.gov/show/NCT01832805). In addition to global brain connectivity and rostral anterior cingulate cortex (rACC) seed‐based functional connectivity (FC), elastic‐net regression and cross‐validation procedures were used to identify baseline intrinsic brain connectivity biomarkers for sham‐rTMS responses. Placebo responses to accelerated sham rTMS were correlated with baseline global brain connectivity in the rACC/ventral medial prefrontal cortex (vmPFC). Concerning the rACC seed‐based FC analysis, the placebo response was associated positively with the precuneus/posterior cingulate (PCun/PCC) cortex and negatively with the middle frontal gyrus. Our findings provide first brain imaging evidence for placebo responses to sham stimulation being predictable from rACC rsFC profiles, especially in brain areas implicated in (re)appraisal and self‐focus processes.
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Affiliation(s)
- Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaowan Wang
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Chris Baeken
- Department of Psychiatry and Medical Psychology, Ghent University, Ghent, Belgium.,Department of Psychiatry, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZBrussel), Brussels, Belgium.,Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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38
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Neural Predictors of the Antidepressant Placebo Response. Pharmaceuticals (Basel) 2019; 12:ph12040158. [PMID: 31635043 PMCID: PMC6958379 DOI: 10.3390/ph12040158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 10/15/2019] [Accepted: 10/18/2019] [Indexed: 02/06/2023] Open
Abstract
The antidepressant placebo response remains a barrier to the development of novel therapies for depression, despite decades of efforts to identify and methodologically address its clinical correlates. This manuscript reviews recent neuroimaging studies that aim to identify the neural signature of antidepressant placebo response. Data captured in clinical trials have primarily focused on antidepressant efficacy or predicting antidepressant response and have reliably implicated the rostral anterior cingulate cortex (rACC) in antidepressant placebo response, but also in medication response. Imaging and electroencephalography (EEG) experiments specifically interrogating the mechanism of antidepressant placebo response, while few, suggest the reward network, including opiate neurotransmission, is also involved. Therefore, while the rACC is likely involved in the antidepressant placebo response, its observation in isolation is unlikely to prospectively distinguish antidepressant placebo from medication responders. Instead, future studies of antidepressant placebo response should probe the reward network as a whole and incorporate sophisticated computational analytical approaches.
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Smith EE, Tenke CE, Deldin PJ, Trivedi MH, Weissman MM, Auerbach RP, Bruder GE, Pizzagalli DA, Kayser J. Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity. Psychophysiology 2019; 57:e13483. [PMID: 31578740 DOI: 10.1111/psyp.13483] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 08/28/2019] [Accepted: 08/29/2019] [Indexed: 12/22/2022]
Abstract
Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.
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Affiliation(s)
- Ezra E Smith
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA
| | - Craig E Tenke
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA.,Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Patricia J Deldin
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Myrna M Weissman
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Randy P Auerbach
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Gerard E Bruder
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.,Center for Depression, Anxiety & Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Jürgen Kayser
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA.,Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, New York, USA
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40
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Riva-Posse P, Holtzheimer PE, Mayberg HS. Cingulate-mediated depressive symptoms in neurologic disease and therapeutics. HANDBOOK OF CLINICAL NEUROLOGY 2019; 166:371-379. [PMID: 31731923 DOI: 10.1016/b978-0-444-64196-0.00021-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The depressive syndrome includes a number of symptoms that are clinically diverse. Research in the past decades has consistently demonstrated that the cingulate cortex plays an essential role in these manifestations. With anatomic studies initially showing volumetric changes, followed by the insights that functional imaging and physiology contributed to neuroscience and psychiatry, the distinct areas of the cingulate subdivisions were seen to have unique contributions. The subcallosal cingulate, with its functional responsivity to mood states and to antidepressant therapies, has been identified as a central node within the mood regulation network. In this chapter, detailed descriptions of the anatomic and functional changes that are seen in depression will be discussed. Finally, a focus on the development of deep brain stimulation in the subcallosal cingulate area will be used to emphasize the conceptualization of a network model with the cingulate as a hub, where engagement of remote areas of the depression network is needed to treat depression.
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Affiliation(s)
- Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Paul E Holtzheimer
- Departments of Psychiatry and Surgery, Geisel School of Medicine at Dartmouth, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States
| | - Helen S Mayberg
- Departments of Neurology, Neurosurgery, Psychiatry, and Neuroscience, Center of Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
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