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Liverani MC, Loukas S, Gui L, Pittet MP, Pereira M, Truttmann AC, Brunner P, Bickle-Graz M, Hüppi PS, Meskaldji DE, Borradori-Tolsa C. Behavioral outcome of very preterm children at 5 years of age: Prognostic utility of brain tissue volumes at term-equivalent-age, perinatal, and environmental factors. Brain Behav 2023; 13:e2818. [PMID: 36639960 PMCID: PMC9927834 DOI: 10.1002/brb3.2818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 10/26/2022] [Accepted: 10/26/2022] [Indexed: 01/15/2023] Open
Abstract
OBJECTIVE Prematurity is associated with a high risk of long-term behavioral problems. This study aimed to assess the prognostic utility of volumetric brain data at term-equivalent-age (TEA), clinical perinatal factors, and parental social economic risk in the prediction of the behavioral outcome at 5 years in a cohort of very preterm infants (VPT, <32 gestational weeks). METHODS T2-weighted magnetic resonance brain images of 80 VPT children were acquired at TEA and automatically segmented into cortical gray matter, deep subcortical gray matter, white matter (WM), cerebellum (CB), and cerebrospinal fluid. The gray matter structure of the amygdala was manually segmented. Children were examined at 5 years of age with a behavioral assessment, using the strengths and difficulties questionnaire (SDQ). The utility of brain volumes at TEA, perinatal factors, and social economic risk for the prediction of behavioral outcome was investigated using support vector machine classifiers and permutation feature importance. RESULTS The predictive modeling of the volumetric data showed that WM, amygdala, and CB volumes were the best predictors of the SDQ emotional symptoms score. Among the perinatal factors, sex, sepsis, and bronchopulmonary dysplasia were the best predictors of the hyperactivity/inattention score. When combining the social economic risk with volumetric and perinatal factors, we were able to accurately predict the emotional symptoms score. Finally, social economic risk was positively correlated with the scores of conduct problems and peer problems. CONCLUSIONS This study provides information on the relation between brain structure at TEA and clinical perinatal factors with behavioral outcome at age 5 years in VPT children. Nevertheless, the overall predictive power of our models is relatively modest, and further research is needed to identify factors associated with subsequent behavioral problems in this population.
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Affiliation(s)
- Maria Chiara Liverani
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland.,Sensorimotor, Affective and Social Development Laboratory, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Serafeim Loukas
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland.,Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Laura Gui
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Marie-Pascale Pittet
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Maricé Pereira
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Anita C Truttmann
- Clinic of Neonatology, Department of Women Mother Child, University Center Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pauline Brunner
- Clinic of Neonatology, Department of Women Mother Child, University Center Hospital and University of Lausanne, Lausanne, Switzerland
| | - Myriam Bickle-Graz
- Follow Up Unit, Department of Women Mother Child, University Center Hospital and University of Lausanne, Lausanne, Switzerland
| | - Petra S Hüppi
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Djalel-Eddine Meskaldji
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland.,Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Cristina Borradori-Tolsa
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
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2
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Loukas S, Lordier L, Meskaldji DE, Filippa M, Sa de Almeida J, Van De Ville D, Hüppi PS. Musical memories in newborns: A resting-state functional connectivity study. Hum Brain Mapp 2022; 43:647-664. [PMID: 34738276 PMCID: PMC8720188 DOI: 10.1002/hbm.25677] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 09/17/2021] [Accepted: 09/20/2021] [Indexed: 11/28/2022] Open
Abstract
Music is known to induce emotions and activate associated memories, including musical memories. In adults, it is well known that music activates both working memory and limbic networks. We have recently discovered that as early as during the newborn period, familiar music is processed differently from unfamiliar music. The present study evaluates music listening effects at the brain level in newborns, by exploring the impact of familiar or first‐time music listening on the subsequent resting‐state functional connectivity in the brain. Using a connectome‐based framework, we describe resting‐state functional connectivity (RS‐FC) modulation after music listening in three groups of newborn infants, in preterm infants exposed to music during their neonatal‐intensive‐care‐unit (NICU) stay, in control preterm, and full‐term infants. We observed modulation of the RS‐FC between brain regions known to be implicated in music and emotions processing, immediately following music listening in all newborn infants. In the music exposed group, we found increased RS‐FC between brain regions known to be implicated in familiar and emotionally arousing music and multisensory processing, and therefore implying memory retrieval and associative memory. We demonstrate a positive correlation between the occurrence of the prior music exposure and increased RS‐FC in brain regions implicated in multisensory and emotional processing, indicating strong engagement of musical memories; and a negative correlation with the Default Mode Network, indicating disengagement due to the aforementioned cognitive processing. Our results describe the modulatory effect of music listening on brain RS‐FC that can be linked to brain correlates of musical memory engrams in preterm infants.
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Affiliation(s)
- Serafeim Loukas
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland.,Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Lara Lordier
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Djalel-Eddine Meskaldji
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland.,Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Manuela Filippa
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Joana Sa de Almeida
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Petra S Hüppi
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
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3
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Sa de Almeida J, Meskaldji DE, Loukas S, Lordier L, Gui L, Lazeyras F, Hüppi PS. Preterm birth leads to impaired rich-club organization and fronto-paralimbic/limbic structural connectivity in newborns. Neuroimage 2020; 225:117440. [PMID: 33039621 DOI: 10.1016/j.neuroimage.2020.117440] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/08/2020] [Accepted: 10/05/2020] [Indexed: 02/06/2023] Open
Abstract
Prematurity disrupts brain development during a critical period of brain growth and organization and is known to be associated with an increased risk of neurodevelopmental impairments. Investigating whole-brain structural connectivity alterations accompanying preterm birth may provide a better comprehension of the neurobiological mechanisms related to the later neurocognitive deficits observed in this population. Using a connectome approach, we aimed to study the impact of prematurity on neonatal whole-brain structural network organization at term-equivalent age. In this cohort study, twenty-four very preterm infants at term-equivalent age (VPT-TEA) and fourteen full-term (FT) newborns underwent a brain MRI exam at term age, comprising T2-weighted imaging and diffusion MRI, used to reconstruct brain connectomes by applying probabilistic constrained spherical deconvolution whole-brain tractography. The topological properties of brain networks were quantified through a graph-theoretical approach. Furthermore, edge-wise connectivity strength was compared between groups. Overall, VPT-TEA infants' brain networks evidenced increased segregation and decreased integration capacity, revealed by an increased clustering coefficient, increased modularity, increased characteristic path length, decreased global efficiency and diminished rich-club coefficient. Furthermore, in comparison to FT, VPT-TEA infants had decreased connectivity strength in various cortico-cortical, cortico-subcortical and intra-subcortical networks, the majority of them being intra-hemispheric fronto-paralimbic and fronto-limbic. Inter-hemispheric connectivity was also decreased in VPT-TEA infants, namely through connections linking to the left precuneus or left dorsal cingulate gyrus - two regions that were found to be hubs in FT but not in VPT-TEA infants. Moreover, posterior regions from Default-Mode-Network (DMN), namely precuneus and posterior cingulate gyrus, had decreased structural connectivity in VPT-TEA group. Our finding that VPT-TEA infants' brain networks displayed increased modularity, weakened rich-club connectivity and diminished global efficiency compared to FT infants suggests a delayed transition from a local architecture, focused on short-range connections, to a more distributed architecture with efficient long-range connections in those infants. The disruption of connectivity in fronto-paralimbic/limbic and posterior DMN regions might underlie the behavioral and social cognition difficulties previously reported in the preterm population.
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Affiliation(s)
- Joana Sa de Almeida
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Djalel-Eddine Meskaldji
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland; Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Serafeim Loukas
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Lara Lordier
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Laura Gui
- Department of Radiology and Medical Informatics, Center of BioMedical Imaging (CIBM), University of Geneva, Geneva, Switzerland
| | - François Lazeyras
- Department of Radiology and Medical Informatics, Center of BioMedical Imaging (CIBM), University of Geneva, Geneva, Switzerland
| | - Petra S Hüppi
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland.
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Meskaldji DE, Van De Ville D, Thiran JP, Morgenthaler S. A comprehensive error rate for multiple testing. Stat Pap (Berl) 2020. [DOI: 10.1007/s00362-018-1008-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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5
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Rodriguez CK, Jagadish AK, Meskaldji DE, Haller S, Hermann F, Van De Ville D, Giannakopoulos P. P2-409: STRUCTURAL CORRELATES OF PERSONALITY DIMENSIONS IN NORMAL AGING AND MILD COGNITIVE IMPAIRMENT. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.2816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
| | | | | | - Sven Haller
- University of Geneva; Faculty of Medicine; Geneva Switzerland
- University Hospital Freiburg; Freiburg Germany
- Uppsala University; Uppsala Sweden
| | - Francois Hermann
- University Hospital of Geneva; Geneva Switzerland
- University Hospitals of Geneva; Geneva Switzerland
| | - Dimitri Van De Ville
- University of Geneva; Faculty of Medicine; Geneva Switzerland
- Ecole Polytechnique Fédérale de Lausanne; Lausanne Switzerland
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6
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Rodriguez C, Jagadish AK, Meskaldji DE, Haller S, Herrmann F, Van De Ville D, Giannakopoulos P. Structural Correlates of Personality Dimensions in Healthy Aging and MCI. Front Psychol 2019; 9:2652. [PMID: 30670999 PMCID: PMC6331460 DOI: 10.3389/fpsyg.2018.02652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 12/10/2018] [Indexed: 12/21/2022] Open
Abstract
The revised NEO Personality Inventory (NEOPI-R), popularly known as the five-factor model, defines five personality factors: Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. The structural correlates of these personality factors are still a matter of debate. In this work, we examine the impact of subtle cognitive deficits on structural substrates of personality in the elderly using DTI derived white matter (WM) integrity measure, Fractional Anisotropy (FA). We employed canonical correlation analysis (CCA) to study the relationship between personality factors of the NEOPI-R and FA measures in two population groups: healthy controls and MCI. Agreeableness was the only personality factor to be associated with FA patterns in both groups. Openness was significantly related to FA data in the MCI group and the inverse was true for Conscientiousness. Furthermore, we generated saliency maps using bootstrapping strategy which revealed a larger number of positive correlations in healthy aging in contrast to the MCI status. The MCI group was found to be associated with a predominance of negative correlations indicating that higher Agreeableness and Openness scores were mostly related to lower FA values in interhemispheric and cortico-spinal tracts and a limited number of higher FA values in cortico-cortical and cortico-subcortical connection. Altogether these findings support the idea that WM microstructure may represent a valid correlate of personality dimensions and also indicate that the presence of early cognitive deficits led to substantial changes in the associations between WM integrity and personality factors.
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Affiliation(s)
- Cristelle Rodriguez
- Division of Institutional Measures, Medical Direction, University Hospitals of Geneva, Geneva, Switzerland
| | - Akshay Kumar Jagadish
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - Djalel-Eddine Meskaldji
- Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,CIRD - Centre d'Imagerie Rive Droite, Geneva, Switzerland
| | - Francois Herrmann
- Division of Geriatrics, Department of Internal Medicine, Rehabilitation and Geriatrics, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Division of Institutional Measures, Medical Direction, University Hospitals of Geneva, Geneva, Switzerland.,Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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7
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Karahanoğlu FI, Baran B, Nguyen QTH, Meskaldji DE, Yendiki A, Vangel M, Santangelo SL, Manoach DS. Diffusion-weighted imaging evidence of altered white matter development from late childhood to early adulthood in Autism Spectrum Disorder. Neuroimage Clin 2018; 19:840-847. [PMID: 29946509 PMCID: PMC6008282 DOI: 10.1016/j.nicl.2018.06.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 05/18/2018] [Accepted: 06/03/2018] [Indexed: 12/01/2022]
Abstract
Autism Spectrum Disorder (ASD) is thought to reflect disrupted development of brain connectivity characterized by white matter abnormalities and dyscoordination of activity across brain regions that give rise to core features. But there is little consensus about the nature, timing and location of white matter abnormalities as quantified with diffusion-weighted MRI. Inconsistent findings likely reflect small sample sizes, motion confounds and sample heterogeneity, particularly different age ranges across studies. We examined the microstructural integrity of major white matter tracts in relation to age in 38 high functioning ASD and 35 typically developing (TD) participants, aged 8-25, whose diffusion-weighted scans met strict data-quality criteria and survived group matching for motion. While there were no overall group differences in diffusion measures, the groups showed different relations with age. Only the TD group showed the expected positive correlations of fractional anisotropy with age. In parallel, axial diffusivity was unrelated to age in TD, but showed inverse correlations with age in ASD. Younger participants with ASD tended to have higher fractional anisotropy and axial diffusivity than their TD peers, while the opposite was true for older participants. Most of the affected tracts - cingulum bundle, inferior and superior longitudinal fasciculi - are association bundles related to cognitive, social and emotional functions that are abnormal in ASD. The manifestations of abnormal white matter development in ASD as measured by diffusion-weighted MRI depend on age and this may contribute to inconsistent findings across studies. We conclude that ASD is characterized by altered white matter development from childhood to early adulthood that may underlie abnormal brain function and contribute to core features.
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Affiliation(s)
- Fikret Işık Karahanoğlu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.
| | - Bengi Baran
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Quynh Trang Huong Nguyen
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Djalel-Eddine Meskaldji
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Mark Vangel
- Department of Biostatistics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Susan L Santangelo
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Maine Medical Center Research Institute, Scarborough, ME, United States; Tufts University School of Medicine, Department of Psychiatry, Boston, MA, United States
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
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8
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Hasler R, Preti MG, Meskaldji DE, Prados J, Adouan W, Rodriguez C, Toma S, Hiller N, Ismaili T, Hofmeister J, Sinanaj I, Baud P, Haller S, Giannakopoulos P, Schwartz S, Perroud N, Van De Ville D. Inter-hemispherical asymmetry in default-mode functional connectivity and BAIAP2 gene are associated with anger expression in ADHD adults. Psychiatry Res Neuroimaging 2017; 269:54-61. [PMID: 28938222 DOI: 10.1016/j.pscychresns.2017.09.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 09/07/2017] [Accepted: 09/07/2017] [Indexed: 11/27/2022]
Abstract
Attention deficit hyperactivity disorder (ADHD) is accompanied by resting-state alterations, including abnormal activity, connectivity and asymmetry of the default-mode network (DMN). Concurrently, recent studies suggested a link between ADHD and the presence of polymorphisms within the gene BAIAP2 (i.e., brain-specific angiogenesis inhibitor 1-associated protein 2), known to be differentially expressed in brain hemispheres. The clinical and neuroimaging correlates of this polymorphism are still unknown. We investigated the association between BAIAP2 polymorphisms and DMN functional connectivity (FC) asymmetry as well as behavioral measures in ADHD adults. Resting-state fMRI was acquired from 30 ADHD and 15 healthy adults. For each subject, rs7210438 and rs8079626 within the gene BAIAP2 were genotyped. ADHD severity, impulsiveness and anger were assessed for the ADHD group. Using multivariate analysis of variance, we found that genetic features do have an impact on DMN FC asymmetry. In particular, polymorphism rs8079626 affects medial frontal gyrus and inferior parietal lobule connectivity asymmetry, lower for AA than AG/GG carriers. Further, when combining FC asymmetry and the presence of the rs8079626 variant, we successfully predicted increased externalization of anger in ADHD. In conclusion, a complex interplay between genetic vulnerability and inter-hemispherical DMN FC asymmetry plays a role in emotion regulation in adult ADHD.
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Affiliation(s)
- R Hasler
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland; Department of Psychiatry, University of Geneva, Switzerland; Department of Neuroscience, Faculty of Medicine of the University of Geneva, Switzerland
| | - M G Preti
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Switzerland.
| | - D E Meskaldji
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Switzerland; Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
| | - J Prados
- Department of Psychiatry, University of Geneva, Switzerland; Department of Neuroscience, Faculty of Medicine of the University of Geneva, Switzerland
| | - W Adouan
- Department of Neuroscience, Faculty of Medicine of the University of Geneva, Switzerland
| | - C Rodriguez
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland
| | - S Toma
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland
| | - N Hiller
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland
| | - T Ismaili
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland
| | - J Hofmeister
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Department of Neuroscience, Faculty of Medicine of the University of Geneva, Switzerland
| | - I Sinanaj
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland; Department of Neuroscience, Faculty of Medicine of the University of Geneva, Switzerland; Swiss Center for Affective Studies, University of Geneva, Switzerland
| | - P Baud
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland
| | - S Haller
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | - P Giannakopoulos
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland; Department of Psychiatry, University of Geneva, Switzerland
| | - S Schwartz
- Department of Psychiatry, University of Geneva, Switzerland
| | - N Perroud
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland; Department of Psychiatry, University of Geneva, Switzerland
| | - D Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Switzerland
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9
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Perrotta G, Bonnier G, Meskaldji DE, Romascano D, Aydarkhanov R, Daducci A, Simioni S, Cavassini M, Metral M, Lazeyras F, Meuli R, Krueger G, Du Pasquier RA, Granziera C. Rivastigmine decreases brain damage in HIV patients with mild cognitive deficits. Ann Clin Transl Neurol 2017; 4:915-920. [PMID: 29296621 PMCID: PMC5740253 DOI: 10.1002/acn3.493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 08/25/2017] [Accepted: 09/22/2017] [Indexed: 11/08/2022] Open
Abstract
Rivastigmine has been shown to improve cognition in HIV+ patients with minor neurocognitive disorders; however, the mechanisms underlying such beneficial effect are currently unknown. To assess whether rivastigmine therapy is associated with decreased brain inflammation and damage, we performed T1/T2* relaxometry and magnetization transfer imaging in 17 aviremic HIV+ patients with minor neurocognitive disorders enrolled on a crossed over randomized rivastigmine trial. Rivastigmine therapy was associated with changes in MRI metrics indicating a decrease in brain water content (i.e., edema reabsorption) and/or reduced demyelination/axonal damage. Furthermore, MRI changes correlated with cognitive improvement on rivastigmine therapy.
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Affiliation(s)
- Gaetano Perrotta
- Department of Clinical Neurosciences, Service of Neurology, Neuroimmunology Unit Lausanne University Hospital and University of Lausanne Lausanne Vaud Switzerland
| | - Guillaume Bonnier
- A.A. Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA
| | - Djalel-Eddine Meskaldji
- Institute of Bioengineering École Polytechnique Fédérale de Lausanne Lausanne Vaud Switzerland.,Department of Radiology and Medical Informatics University of Geneva Geneva Switzerland.,Applied Statistics, Institute of Mathematics École Polytechnique Fédérale de Lausanne Lausanne Vaud Switzerland
| | - David Romascano
- Signal Processing Laboratory (LTS5) École Polytechnique Fédérale de Lausanne Lausanne Vaud Switzerland
| | | | - Alessandro Daducci
- Signal Processing Laboratory (LTS5) École Polytechnique Fédérale de Lausanne Lausanne Vaud Switzerland
| | - Samanta Simioni
- Department of Clinical Neurosciences, Service of Neurology, Neuroimmunology Unit Lausanne University Hospital and University of Lausanne Lausanne Vaud Switzerland
| | - Matthias Cavassini
- Department of Infectious Diseases Lausanne University Hospital and University of Lausanne Lausanne Vaud Switzerland
| | - Melanie Metral
- Department of Clinical Neurosciences, Service of Neurology, Neuroimmunology Unit Lausanne University Hospital and University of Lausanne Lausanne Vaud Switzerland
| | - François Lazeyras
- Department of Radiology Geneva University Hospital and University of Geneva Geneva Switzerland
| | - Reto Meuli
- Department of Radiology Lausanne University Hospital and University of Lausanne Lausanne Vaud Switzerland
| | | | - Renaud A Du Pasquier
- Department of Clinical Neurosciences, Service of Neurology, Neuroimmunology Unit Lausanne University Hospital and University of Lausanne Lausanne Vaud Switzerland
| | - Cristina Granziera
- Department of Clinical Neurosciences, Service of Neurology, Neuroimmunology Unit Lausanne University Hospital and University of Lausanne Lausanne Vaud Switzerland.,A.A. Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA
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10
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Meskaldji DE, Preti MG, Bolton TA, Montandon ML, Rodriguez C, Morgenthaler S, Giannakopoulos P, Haller S, Van De Ville D. Prediction of long-term memory scores in MCI based on resting-state fMRI. Neuroimage Clin 2016; 12:785-795. [PMID: 27812505 PMCID: PMC5079359 DOI: 10.1016/j.nicl.2016.10.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 09/16/2016] [Accepted: 10/06/2016] [Indexed: 12/11/2022]
Abstract
Resting-state functional MRI (rs-fMRI) opens a window on large-scale organization of brain function. However, establishing relationships between resting-state brain activity and cognitive or clinical scores is still a difficult task, in particular in terms of prediction as would be meaningful for clinical applications such as early diagnosis of Alzheimer's disease. In this work, we employed partial least square regression under cross-validation scheme to predict episodic memory performance from functional connectivity (FC) patterns in a set of fifty-five MCI subjects for whom rs-fMRI acquisition and neuropsychological evaluation was carried out. We show that a newly introduced FC measure capturing the moments of anti-correlation between brain areas, discordance, contains key information to predict long-term memory scores in MCI patients, and performs better than standard measures of correlation to do so. Our results highlighted that stronger discordance within default mode network (DMN) areas, as well as across DMN, attentional and limbic networks, favor episodic memory performance in MCI. We use PLS to predict memory scores from resting-state fMRI. We compare prediction performance of different functional connectivity measures. We highlight the role of anti-correlation in memory-score prediction. We highlight the role of default-mode network in episodic memory.
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Affiliation(s)
- Djalel-Eddine Meskaldji
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maria Giulia Preti
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Thomas Aw Bolton
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marie-Louise Montandon
- Divisions of Diagnostic and Interventional Neuroradiology, Geneva University Hospitals, Geneva, Switzerland
| | | | - Stephan Morgenthaler
- Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Sven Haller
- Affidea CDRC - Centre Diagnostique Radiologique de Carouge, Switzerland; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden; Department of Neuroradiology, University Hospital Freiburg, Germany; Faculty of Medicine of the University of Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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Meskaldji DE, Vasung L, Romascano D, Thiran JP, Hagmann P, Morgenthaler S, Van De Ville D. Improved statistical evaluation of group differences in connectomes by screening-filtering strategy with application to study maturation of brain connections between childhood and adolescence. Neuroimage 2014; 108:251-64. [PMID: 25498390 DOI: 10.1016/j.neuroimage.2014.11.059] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 10/30/2014] [Accepted: 11/29/2014] [Indexed: 11/17/2022] Open
Abstract
Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the data structure and the prior information concerning positive dependence between nodes and connections, without relying on strong assumptions. As a first step, we decompose the brain network, i.e., the connectome, into subnetworks and we apply a screening at the subnetwork level. The subnetworks are defined either according to prior knowledge or by applying a data driven algorithm. Given the results of the screening step, a filtering is performed to seek real differences at the node/connection level. The proposed strategy could be used to strongly control either the family-wise error rate or the false discovery rate. We show by means of different simulations the benefit of the proposed strategy, and we present a real application of comparing connectomes of preschool children and adolescents.
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Affiliation(s)
- Djalel-Eddine Meskaldji
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Lana Vasung
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - David Romascano
- LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology, University Hospital Center and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, University Hospital Center and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Stephan Morgenthaler
- Applied Statistics, Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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Romascano D, Meskaldji DE, Bonnier G, Simioni S, Rotzinger D, Lin YC, Menegaz G, Roche A, Schluep M, Pasquier RD, Richiardi J, Van De Ville D, Daducci A, Sumpf T, Fraham J, Thiran JP, Krueger G, Granziera C. Multicontrast connectometry: a new tool to assess cerebellum alterations in early relapsing-remitting multiple sclerosis. Hum Brain Mapp 2014; 36:1609-19. [PMID: 25421928 DOI: 10.1002/hbm.22698] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Revised: 11/06/2014] [Accepted: 11/17/2014] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Cerebellar pathology occurs in late multiple sclerosis (MS) but little is known about cerebellar changes during early disease stages. In this study, we propose a new multicontrast "connectometry" approach to assess the structural and functional integrity of cerebellar networks and connectivity in early MS. METHODS We used diffusion spectrum and resting-state functional MRI (rs-fMRI) to establish the structural and functional cerebellar connectomes in 28 early relapsing-remitting MS patients and 16 healthy controls (HC). We performed multicontrast "connectometry" by quantifying multiple MRI parameters along the structural tracts (generalized fractional anisotropy-GFA, T1/T2 relaxation times and magnetization transfer ratio) and functional connectivity measures. Subsequently, we assessed multivariate differences in local connections and network properties between MS and HC subjects; finally, we correlated detected alterations with lesion load, disease duration, and clinical scores. RESULTS In MS patients, a subset of structural connections showed quantitative MRI changes suggesting loss of axonal microstructure and integrity (increased T1 and decreased GFA, P < 0.05). These alterations highly correlated with motor, memory and attention in patients, but were independent of cerebellar lesion load and disease duration. Neither network organization nor rs-fMRI abnormalities were observed at this early stage. CONCLUSION Multicontrast cerebellar connectometry revealed subtle cerebellar alterations in MS patients, which were independent of conventional disease markers and highly correlated with patient function. Future work should assess the prognostic value of the observed damage.
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Affiliation(s)
- David Romascano
- Advanced Clinical Imaging Technology, Siemens Healthcare IM BM PI & Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne (CHUV), Lausanne, Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland
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Fischi-Gómez E, Vasung L, Meskaldji DE, Lazeyras F, Borradori-Tolsa C, Hagmann P, Barisnikov K, Thiran JP, Hüppi PS. Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition. Cereb Cortex 2014; 25:2793-805. [DOI: 10.1093/cercor/bhu073] [Citation(s) in RCA: 140] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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