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Denier N, Grieder M, Jann K, Breit S, Mertse N, Walther S, Soravia LM, Meyer A, Federspiel A, Wiest R, Bracht T. Analyzing fractal dimension in electroconvulsive therapy: Unraveling complexity in structural and functional neuroimaging. Neuroimage 2024; 297:120671. [PMID: 38901774 DOI: 10.1016/j.neuroimage.2024.120671] [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: 02/19/2024] [Revised: 05/21/2024] [Accepted: 06/06/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response. METHODS Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12's surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome. RESULTS Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome. CONCLUSION This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.
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Affiliation(s)
- Niklaus Denier
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.
| | - Matthias Grieder
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Kay Jann
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Sigrid Breit
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Nicolas Mertse
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Leila M Soravia
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Agnes Meyer
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Andrea Federspiel
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland; Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland; Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Tobias Bracht
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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Joo YY, Lee E, Kim BG, Kim G, Seo J, Cha J. Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595444. [PMID: 38826224 PMCID: PMC11142157 DOI: 10.1101/2024.05.22.595444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.
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Affiliation(s)
- Yoonjung Yoonie Joo
- Department of Psychology, Seoul National University
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Eunji Lee
- Department of Psychology, Seoul National University
| | - Bo-Gyeom Kim
- Department of Psychology, Seoul National University
| | - Gakyung Kim
- Department of Brain and Cognitive Sciences, Seoul National University
| | - Jungwoo Seo
- Department of Brain and Cognitive Sciences, Seoul National University
| | - Jiook Cha
- Department of Psychology, Seoul National University
- Department of Brain and Cognitive Sciences, Seoul National University
- Institute of Psychological Science, Seoul National University, Seoul, South Korea
- Graduate School of Artificial Intelligence, Seoul National University, Seoul, South Korea
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Liu S, Abdellaoui A, Verweij KJH, van Wingen GA. Gene Expression has Distinct Associations with Brain Structure and Function in Major Depressive Disorder. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2205486. [PMID: 36638259 PMCID: PMC9982587 DOI: 10.1002/advs.202205486] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Major depressive disorder (MDD) is associated with structural and functional brain abnormalities. MDD as well as brain anatomy and function are influenced by genetic factors, but the role of gene expression remains unclear. Here, this work investigates how cortical gene expression contributes to structural and functional brain abnormalities in MDD. This work compares the gray matter volume and resting-state functional measures in a Chinese sample of 848 MDD patients and 749 healthy controls, and these case-control differences are then associated with cortical variation of gene expression. While whole gene expression is positively associated with structural abnormalities, it is negatively associated with functional abnormalities. This work observes the relationships of expression levels with brain abnormalities for individual genes, and found that transcriptional correlates of brain structure and function show opposite relations with gene dysregulation in postmortem cortical tissue from MDD patients. This work further identifies genes that are positively or negatively related to structural abnormalities as well as functional abnormalities. The MDD-related genes are enriched for brain tissue, cortical cells, and biological pathways. These findings suggest that distinct genetic mechanisms underlie structural and functional brain abnormalities in MDD, and highlight the importance of cortical gene expression for the development of cortical abnormalities.
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Affiliation(s)
- Shu Liu
- Amsterdam UMC locationUniversity of AmsterdamDepartment of PsychiatryAmsterdam Neuroscience, AmsterdamMeibergdreef 5Amsterdam1100 DDThe Netherlands
| | - Abdel Abdellaoui
- Amsterdam UMC locationUniversity of AmsterdamDepartment of PsychiatryAmsterdam Neuroscience, AmsterdamMeibergdreef 5Amsterdam1100 DDThe Netherlands
| | - Karin J. H. Verweij
- Amsterdam UMC locationUniversity of AmsterdamDepartment of PsychiatryAmsterdam Neuroscience, AmsterdamMeibergdreef 5Amsterdam1100 DDThe Netherlands
| | - Guido A. van Wingen
- Amsterdam UMC locationUniversity of AmsterdamDepartment of PsychiatryAmsterdam Neuroscience, AmsterdamMeibergdreef 5Amsterdam1100 DDThe Netherlands
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Kuo SS, Roalf DR, Prasad KM, Musket CW, Rupert PE, Wood J, Gur RC, Almasy L, Gur RE, Nimgaonkar VL, Pogue-Geile MF. Age-dependent effects of schizophrenia genetic risk on cortical thickness and cortical surface area: Evaluating evidence for neurodevelopmental and neurodegenerative models of schizophrenia. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2022; 131:674-688. [PMID: 35737559 PMCID: PMC9339500 DOI: 10.1037/abn0000765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Risk for schizophrenia peaks during early adulthood, a critical period for brain development. Although several influential theoretical models have been proposed for the developmental relationship between brain pathology and clinical onset, to our knowledge, no study has directly evaluated the predictions of these models for schizophrenia developmental genetic effects on brain structure. To address this question, we introduce a framework to estimate the effects of schizophrenia genetic variation on brain structure phenotypes across the life span. Five-hundred and six participants, including 30 schizophrenia probands, 200 of their relatives (aged 12-85 years) from 32 families with at least two first-degree schizophrenia relatives, and 276 unrelated controls, underwent MRI to assess regional cortical thickness (CT) and cortical surface area (CSA). Genetic variance decomposition analyses were conducted to distinguish among schizophrenia neurogenetic effects that are most salient before schizophrenia peak age-of-risk (i.e., early neurodevelopmental effects), after peak age-of-risk (late neurodevelopmental effects), and during the later plateau of age-of-risk (neurodegenerative effects). Genetic correlations between schizophrenia and cortical traits suggested early neurodevelopmental effects for frontal and insula CSA, late neurodevelopmental effects for overall CSA and frontal, parietal, and occipital CSA, and possible neurodegenerative effects for temporal CT and parietal CSA. Importantly, these developmental neurogenetic effects were specific to schizophrenia and not found with nonpsychotic depression. Our findings highlight the potentially dynamic nature of schizophrenia genetic effects across the lifespan and emphasize the utility of integrating neuroimaging methods with developmental behavior genetic approaches to elucidate the nature and timing of risk-conferring processes in psychopathology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Sandu AL, Waiter GD, Staff RT, Nazlee N, Habota T, McNeil CJ, Chapko D, Williams JH, Fall CHD, Chandak GR, Pene S, Krishna M, McIntosh AM, Whalley HC, Kumaran K, Krishnaveni GV, Murray AD. Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study. Sci Rep 2022; 12:11025. [PMID: 35773463 PMCID: PMC9247090 DOI: 10.1038/s41598-022-15208-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 06/20/2022] [Indexed: 01/20/2023] Open
Abstract
Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.
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Affiliation(s)
- Anca-Larisa Sandu
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK.
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Roger T Staff
- Aberdeen Royal Infirmary, NHS Grampian, Aberdeen, UK
| | - Nafeesa Nazlee
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Tina Habota
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Chris J McNeil
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Dorota Chapko
- School of Public Health, Imperial College London, London, UK
| | | | - Caroline H D Fall
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases, CSIR - Centre for Cellular and Molecular Biology, Hyderabad, India
| | - Shailesh Pene
- Department of Imaging and Interventional Radiology, Narayana Multispecialty Hospital, Mysore, India
| | - Murali Krishna
- Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kalyanaraman Kumaran
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | | | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
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Trevisan N, Miola A, Cattarinussi G, Kubera KM, Hirjak D, Wolf RC, Sambataro F. Cortical folding complexity is distinctively altered in schizophrenia and bipolar disorder. Schizophr Res 2022; 241:92-93. [PMID: 35101838 DOI: 10.1016/j.schres.2022.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/10/2022] [Accepted: 01/16/2022] [Indexed: 12/23/2022]
Affiliation(s)
- Nicolò Trevisan
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Alessandro Miola
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Katharina M Kubera
- Department of General Psychiatry at the Center for Psychosocial Medicine, Heidelberg University, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Robert Christian Wolf
- Department of General Psychiatry at the Center for Psychosocial Medicine, Heidelberg University, Germany
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy.
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Meregalli V, Alberti F, Madan CR, Meneguzzo P, Miola A, Trevisan N, Sambataro F, Favaro A, Collantoni E. Cortical Complexity Estimation Using Fractal Dimension: A Systematic Review of the Literature on Clinical and Nonclinical Samples. Eur J Neurosci 2022; 55:1547-1583. [PMID: 35229388 PMCID: PMC9313853 DOI: 10.1111/ejn.15631] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/27/2022] [Accepted: 02/20/2022] [Indexed: 12/04/2022]
Abstract
Fractal geometry has recently been proposed as a useful tool for characterizing the complexity of the brain cortex, which is likely to derive from the recurrence of sulci–gyri convolution patterns. The index used to describe the cortical complexity is called fractal dimensional (FD) and was employed by different research exploring the neurobiological correlates of distinct pathological and nonpathological conditions. This review aims to describe the literature on the application of this index, summarize the heterogeneities between studies and inform future research on this topic. Sixty‐two studies were included in the systematic review. The main research lines concern neurodevelopment, aging and the neurobiology of specific psychiatric and neurological disorders. Overall, the included papers indicate that cortical complexity is likely to reduce during aging and in various pathological processes affecting the brain. Nevertheless, the high heterogeneity between studies strongly prevents the possibility of drawing conclusions. Further research considering this index besides other morphological values is needed to better clarify the role of FD in characterizing the cortical structure. Fractal dimension (FD) is a useful tool for the assessment of cortical complexity. In healthy controls, FD is associated with development, aging and cognition. Alterations in FD have been observed in different neurological and psychiatric disorders.
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Affiliation(s)
- Valentina Meregalli
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | | | | | - Paolo Meneguzzo
- Department of Neurosciences, University of Padua, Padova, Italy
| | - Alessandro Miola
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Nicolò Trevisan
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Fabio Sambataro
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Angela Favaro
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
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