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Kotila A, Tohka J, Kauppi JP, Gabbatore I, Mäkinen L, Hurtig TM, Ebeling HE, Korhonen V, Kiviniemi VJ, Loukusa S. Neural-level associations of non-verbal pragmatic comprehension in young Finnish autistic adults. Int J Circumpolar Health 2021; 80:1909333. [PMID: 34027832 PMCID: PMC8158210 DOI: 10.1080/22423982.2021.1909333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/12/2021] [Accepted: 03/23/2021] [Indexed: 11/18/2022] Open
Abstract
This video-based study examines the pragmatic non-verbal comprehension skills and corresponding neural-level findings in young Finnish autistic adults, and controls. Items from the Assessment Battery of Communication (ABaCo) were chosen to evaluate the comprehension of non-verbal communication. Inter-subject correlation (ISC) analysis of the functional magnetic resonance imaging data was used to reveal the synchrony of brain activation across participants during the viewing of pragmatically complex scenes of ABaCo videos. The results showed a significant difference between the ISC maps of the autistic and control groups in tasks involving the comprehension of non-verbal communication, thereby revealing several brain regions where correlation of brain activity was greater within the control group. The results suggest a possible weaker modulation of brain states in response to the pragmatic non-verbal communicative situations in autistic participants. Although there was no difference between the groups in behavioural responses to ABaCo items, there was more variability in the accuracy of the responses in the autistic group. Furthermore, mean answering and reaction times correlated with the severity of autistic traits. The results indicate that even if young autistic adults may have learned to use compensatory resources in their communicative-pragmatic comprehension, pragmatic processing in naturalistic situations still requires additional effort.
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Affiliation(s)
- Aija Kotila
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
| | - Jussi Tohka
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jukka-Pekka Kauppi
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Ilaria Gabbatore
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
- Department of Psychology, University of Turin, Turin, Italy
| | - Leena Mäkinen
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
| | - Tuula M. Hurtig
- Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
- Research Unit of Clinical Neuroscience, Psychiatry, University of Oulu
| | - Hanna E. Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital and Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa J. Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital and Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
- Oulu Functional NeuroImaging-lab, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
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Paakki J, Rahko JS, Kotila A, Mattila M, Miettunen H, Hurtig TM, Jussila KK, Kuusikko‐Gauffin S, Moilanen IK, Tervonen O, Kiviniemi VJ. Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study. Brain Behav 2021; 11:e02174. [PMID: 33998178 PMCID: PMC8213933 DOI: 10.1002/brb3.2174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 04/05/2021] [Accepted: 04/23/2021] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION There has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common. METHODS Inspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns. RESULTS We found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation. CONCLUSION Nonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.
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Affiliation(s)
- Jyri‐Johan Paakki
- Faculty of Medicine, Health and Biosciences Doctoral ProgrammeUniversity of Oulu Graduate SchoolUniversity of OuluOuluFinland
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
| | - Jukka S. Rahko
- Faculty of Medicine, Health and Biosciences Doctoral ProgrammeUniversity of Oulu Graduate SchoolUniversity of OuluOuluFinland
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Aija Kotila
- Faculty of HumanitiesResearch Unit of LogopedicsUniversity of OuluOuluFinland
| | - Marja‐Leena Mattila
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Helena Miettunen
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Tuula M. Hurtig
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
- Research Unit of Clinical Neuroscience, PsychiatryUniversity of OuluOuluFinland
| | - Katja K. Jussila
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Sanna Kuusikko‐Gauffin
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Irma K. Moilanen
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Osmo Tervonen
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
| | - Vesa J. Kiviniemi
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
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Kotila A, Hyvärinen A, Mäkinen L, Leinonen E, Hurtig T, Ebeling H, Korhonen V, Kiviniemi VJ, Loukusa S. Processing of pragmatic communication in ASD: a video-based brain imaging study. Sci Rep 2020; 10:21739. [PMID: 33303942 PMCID: PMC7729953 DOI: 10.1038/s41598-020-78874-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 06/24/2020] [Accepted: 11/30/2020] [Indexed: 01/24/2023] Open
Abstract
Social and pragmatic difficulties in autism spectrum disorder (ASD) are widely recognized, although their underlying neural level processing is not well understood. The aim of this study was to examine the activity of the brain network components linked to social and pragmatic understanding in order to reveal whether complex socio-pragmatic events evoke differences in brain activity between the ASD and control groups. Nineteen young adults (mean age 23.6 years) with ASD and 19 controls (mean age 22.7 years) were recruited for the study. The stimulus data consisted of video clips showing complex social events that demanded processing of pragmatic communication. In the analysis, the functional magnetic resonance imaging signal responses of the selected brain network components linked to social and pragmatic information processing were compared. Although the processing of the young adults with ASD was similar to that of the control group during the majority of the social scenes, differences between the groups were found in the activity of the social brain network components when the participants were observing situations with concurrent verbal and non-verbal communication events. The results suggest that the ASD group had challenges in processing concurrent multimodal cues in complex pragmatic communication situations.
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Affiliation(s)
- Aija Kotila
- Research Unit of Logopedics, Faculty of Humanities, University of Oulu, Oulu, Finland.
| | - Aapo Hyvärinen
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Leena Mäkinen
- Research Unit of Logopedics, Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Eeva Leinonen
- Office of the Vice Chancellor, Murdoch University, Murdoch, WA, Australia
| | - Tuula Hurtig
- Research Unit of Clinical Neuroscience, Psychiatry, University of Oulu, Oulu, Finland
- PEDEGO Research Unit, The Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Child Psychiatry, Faculty of Medicine, Institute of Clinical Medicine, Oulu University Hospital, Oulu, Finland
| | - Hanna Ebeling
- PEDEGO Research Unit, The Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Child Psychiatry, Faculty of Medicine, Institute of Clinical Medicine, Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), University and University Hospital of Oulu, Oulu, Finland
- Research Unit of Medical Imaging, Physics and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa J Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), University and University Hospital of Oulu, Oulu, Finland
- Research Unit of Medical Imaging, Physics and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, Faculty of Humanities, University of Oulu, Oulu, Finland
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Lieslehto J, Kiviniemi VJ, Nordström T, Barnett JH, Murray GK, Jones PB, Paus T, Veijola J. Polygenic Risk Score for Schizophrenia and Face-Processing Network in Young Adulthood. Schizophr Bull 2019; 45:835-845. [PMID: 30281090 PMCID: PMC6581147 DOI: 10.1093/schbul/sby139] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Development of schizophrenia relates to both genetic and environmental factors. Functional deficits in many cognitive domains, including the ability to communicate in social interactions and impaired recognition of facial expressions, are common for patients with schizophrenia and might also be present in individuals at risk of developing schizophrenia. Here we explore whether an individual's polygenic risk score (PRS) for schizophrenia is associated with the degree of interregional similarities in blood oxygen level-dependent (BOLD) signal and gray matter volume of the face-processing network and whether the exposure to early adversity moderates this association. A total of 90 individuals (mean age 22 years, both functional and structural data available) were used for discovery analyses, and 211 individuals (mean age 26 years, structural data available) were used for replication of the structural findings. Both samples were drawn from the Northern Finland Birth Cohort 1986. We found that the degree of interregional similarities in BOLD signal and gray matter volume vary as a function of PRS; lowest interregional correlation (both measures) was observed in individuals with high PRS. We also replicated the gray matter volume finding. We did not find evidence for an interaction between early adversity and PRS on the interregional correlation of BOLD signal and gray matter volume. We speculate that the observed group differences in PRS-related correlations in both modalities may result from differences in the concurrent functional engagement of the face-processing regions over time, eg, via differences in exposure to social interaction with other people.
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Affiliation(s)
- Johannes Lieslehto
- Department of Psychiatry, Research Unit of Clinical Neuroscience, Faculty of Medicine, University of Oulu, Oulu, Finland,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland,To whom correspondence should be addressed; PO Box 5000, Oulu 90014, Finland; tel: +358-40-125-3267, e-mail: johannes.lieslehto@.gmail.com
| | - Vesa J Kiviniemi
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Tanja Nordström
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland,Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Jennifer H Barnett
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK,Cambridge Cognition Ltd, Cambridge, UK
| | - Graham K Murray
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Peter B Jones
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Tomáš Paus
- Child Mind Institute, New York, NY,Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada,Department of Psychology, University of Toronto, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Juha Veijola
- Department of Psychiatry, Research Unit of Clinical Neuroscience, Faculty of Medicine, University of Oulu, Oulu, Finland,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland,Department of Psychiatry, Oulu University Hospital, Oulu, Finland
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Huotari N, Raitamaa L, Helakari H, Kananen J, Raatikainen V, Rasila A, Tuovinen T, Kantola J, Borchardt V, Kiviniemi VJ, Korhonen VO. Sampling Rate Effects on Resting State fMRI Metrics. Front Neurosci 2019; 13:279. [PMID: 31001071 PMCID: PMC6454039 DOI: 10.3389/fnins.2019.00279] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [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: 10/26/2018] [Accepted: 03/08/2019] [Indexed: 01/21/2023] Open
Abstract
Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.
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Affiliation(s)
- Niko Huotari
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Lauri Raitamaa
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Heta Helakari
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Janne Kananen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Ville Raatikainen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Aleksi Rasila
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Timo Tuovinen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Jussi Kantola
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Viola Borchardt
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Vesa J Kiviniemi
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Vesa O Korhonen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
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6
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Tuovinen T, Rytty R, Moilanen V, Abou Elseoud A, Veijola J, Remes AM, Kiviniemi VJ. The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer's Disease and bvFTD. Front Hum Neurosci 2017; 10:680. [PMID: 28119587 PMCID: PMC5220074 DOI: 10.3389/fnhum.2016.00680] [Citation(s) in RCA: 17] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 12/20/2016] [Indexed: 12/12/2022] Open
Abstract
Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.
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Affiliation(s)
- Timo Tuovinen
- Department of Diagnostic Radiology, Oulu University HospitalOulu, Finland; Oulu Functional NeuroImaging group, Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of OuluOulu, Finland; Medical Research Center Oulu, Oulu University HospitalOulu, Finland
| | - Riikka Rytty
- Department of Diagnostic Radiology, Oulu University HospitalOulu, Finland; Oulu Functional NeuroImaging group, Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of OuluOulu, Finland; Medical Research Center Oulu, Oulu University HospitalOulu, Finland; Research Unit of Clinical Neuroscience, Faculty of Medicine, University of OuluOulu, Finland
| | - Virpi Moilanen
- Research Unit of Clinical Neuroscience, Faculty of Medicine, University of Oulu Oulu, Finland
| | - Ahmed Abou Elseoud
- Department of Diagnostic Radiology, Oulu University HospitalOulu, Finland; Oulu Functional NeuroImaging group, Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of OuluOulu, Finland
| | - Juha Veijola
- Medical Research Center Oulu, Oulu University HospitalOulu, Finland; Research Unit of Clinical Neuroscience, Faculty of Medicine, University of OuluOulu, Finland
| | - Anne M Remes
- Medical Research Center Oulu, Oulu University HospitalOulu, Finland; Department of Neurology, Institute of Clinical Medicine, University of Eastern FinlandKuopio, Finland; Department of Neurology, Kuopio University HospitalKuopio, Finland
| | - Vesa J Kiviniemi
- Department of Diagnostic Radiology, Oulu University HospitalOulu, Finland; Oulu Functional NeuroImaging group, Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of OuluOulu, Finland; Medical Research Center Oulu, Oulu University HospitalOulu, Finland
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7
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Nissilä JS, Mänttäri SK, Särkioja TT, Tuominen HJ, Takala TE, Kiviniemi VJ, Sormunen RT, Saarela SYO, Timonen MJ. The distribution of melanopsin (OPN4) protein in the human brain. Chronobiol Int 2016; 34:37-44. [PMID: 27690288 DOI: 10.1080/07420528.2016.1232269] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Until now, melanopsin (OPN4) - a specialized photopigment being responsive especially to blue light wavelengths - has not been found in the human brain at protein level outside the retina. More specifically, OPN4 has only been found in about 2% of retinal ganglion cells (i.e. in intrinsically photosensitive retinal ganglion cells), and in a subtype of retinal cone-cells. Given that Allen Institute for Brain Science has described a wide distribution of OPN4 mRNA in two human brains, we aimed to investigate whether OPN4 is present in the human brain also at protein level. Western blotting and immunohistochemistry, as well as immunoelectron microscopy, were used to analyse the existence and distribution of OPN4 protein in 18 investigated areas of the human brain in samples obtained in forensic autopsies from 10 male subjects (54 ± 3.5 years). OPN4 protein expression was found in all subjects, and, furthermore, in 5 out of 10 subjects in all investigated brain areas localized in membranous compartments and cytoplasmic vesicles of neurons. To our opinion, the wide distribution of OPN4 in central areas of the human brain evokes a question whether ambient light has important straight targets in the human brain outside the retinohypothalamic tract (RHT). Further studies are, however, needed to investigate the putative physiological phototransductive actions of inborn OPN4 protein outside the RHT in the human brain.
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Affiliation(s)
- Juuso S Nissilä
- a University of Oulu, Center for Life Course Health Research , Oulu , Finland.,b Department of Biology, University of Oulu , Oulu , Finland
| | - Satu K Mänttäri
- b Department of Biology, University of Oulu , Oulu , Finland
| | - Terttu T Särkioja
- c University of Oulu , Institute of Diagnostics, Forensic Medicine , Oulu , Finland
| | - Hannu J Tuominen
- d University of Oulu , Institute of Diagnostics, Pathology , Oulu , Finland.,e Department of Pathology , Oulu University Hospital , Oulu , Finland
| | | | - Vesa J Kiviniemi
- g Department of Diagnostic Radiology , Oulu University Hospital , Oulu , Finland
| | - Raija T Sormunen
- d University of Oulu , Institute of Diagnostics, Pathology , Oulu , Finland.,e Department of Pathology , Oulu University Hospital , Oulu , Finland.,h Biocenter Oulu , University of Oulu , Oulu , Finland
| | | | - Markku J Timonen
- a University of Oulu, Center for Life Course Health Research , Oulu , Finland
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8
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Littow H, Huossa V, Karjalainen S, Jääskeläinen E, Haapea M, Miettunen J, Tervonen O, Isohanni M, Nikkinen J, Veijola J, Murray G, Kiviniemi VJ. Aberrant Functional Connectivity in the Default Mode and Central Executive Networks in Subjects with Schizophrenia - A Whole-Brain Resting-State ICA Study. Front Psychiatry 2015; 6:26. [PMID: 25767449 PMCID: PMC4341512 DOI: 10.3389/fpsyt.2015.00026] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 02/09/2015] [Indexed: 01/04/2023] Open
Abstract
Neurophysiological changes of schizophrenia are currently linked to disturbances in connectivity between functional brain networks. Functional magnetic resonance imaging studies on schizophrenia have focused on a few selected networks. Also previously, it has not been possible to discern whether the functional alterations in schizophrenia originate from spatial shifting or amplitude alterations of functional connectivity. In this study, we aim to discern the differences in schizophrenia patients with respect to spatial shifting vs. signal amplitude changes in functional connectivity in the whole-brain connectome. We used high model order-independent component analysis to study some 40 resting-state networks (RSN) covering the whole cortex. Group differences were analyzed with dual regression coupled with y-concat correction for multiple comparisons. We investigated the RSNs with and without variance normalization in order to discern spatial shifting from signal amplitude changes in 43 schizophrenia patients and matched controls from the Northern Finland 1966 Birth Cohort. Voxel-level correction for multiple comparisons revealed 18 RSNs with altered functional connectivity, 6 of which had both spatial and signal amplitude changes. After adding the multiple comparison, y-concat correction to the analysis for including the 40 RSNs as well, we found that four RSNs showed still changes. These robust changes actually seem encompass parcellations of the default mode network and central executive networks. These networks both have spatially shifted connectivity and abnormal signal amplitudes. Interestingly the networks seem to mix their functional representations in areas like left caudate nucleus and dorsolateral prefrontal cortex. These changes overlapped with areas that have been related to dopaminergic alterations in patients with schizophrenia compared to controls.
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Affiliation(s)
- Harri Littow
- Department of Radiology, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Ville Huossa
- Department of Radiology, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Sami Karjalainen
- Department of Psychiatry, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Erika Jääskeläinen
- Department of Psychiatry, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Marianne Haapea
- Department of Psychiatry, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Jouko Miettunen
- Department of Psychiatry, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Osmo Tervonen
- Department of Radiology, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Matti Isohanni
- Department of Psychiatry, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Juha Nikkinen
- Department of Oncology, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Juha Veijola
- Department of Psychiatry, Medical Research Center, Oulu University Hospital , Oulu , Finland
| | - Graham Murray
- Department of Psychiatry, University of Cambridge , Cambridge , UK
| | - Vesa J Kiviniemi
- Department of Radiology, Medical Research Center, Oulu University Hospital , Oulu , Finland
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9
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Tulppo MP, Jurvelin H, Roivainen E, Nissilä J, Hautala AJ, Kiviniemi AM, Kiviniemi VJ, Takala T. Effects of bright light treatment on psychomotor speed in athletes. Front Physiol 2014; 5:184. [PMID: 24860513 PMCID: PMC4026757 DOI: 10.3389/fphys.2014.00184] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [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: 02/18/2014] [Accepted: 04/23/2014] [Indexed: 11/13/2022] Open
Abstract
Purpose: A recent study suggests that transcranial brain targeted light treatment via ear canals may have physiological effects on brain function studied by functional magnetic resonance imaging (fMRI) techniques in humans. We tested the hypothesis that bright light treatment could improve psychomotor speed in professional ice hockey players. Methods: Psychomotor speed tests with audio and visual warning signals were administered to a Finnish National Ice Hockey League team before and after 24 days of transcranial bright light or sham treatment. The treatments were given during seasonal darkness in the Oulu region (latitude 65 degrees north) when the strain on the players was also very high (10 matches during 24 days). A daily 12-min dose of bright light or sham (n = 11 for both) treatment was given every morning between 8 and 12 am at home with a transcranial bright light device. Mean reaction time and motor time were analyzed separately for both psychomotor tests. Analysis of variance for repeated measures adjusted for age was performed. Results: Time × group interaction for motor time with a visual warning signal was p = 0.024 after adjustment for age. In Bonferroni post-hoc analysis, motor time with a visual warning signal decreased in the bright light treatment group from 127 ± 43 to 94 ± 26 ms (p = 0.024) but did not change significantly in the sham group 121 ± 23 vs. 110 ± 32 ms (p = 0.308). Reaction time with a visual signal did not change in either group. Reaction or motor time with an audio warning signal did not change in either the treatment or sham group. Conclusion: Psychomotor speed, particularly motor time with a visual warning signal, improves after transcranial bright light treatment in professional ice-hockey players during the competition season in the dark time of the year.
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Affiliation(s)
- Mikko P Tulppo
- Department of Exercise and Medical Physiology Verve, Oulu, Finland
| | - Heidi Jurvelin
- Department of General Practice, Institute of Health Sciences, University of Oulu Oulu, Finland
| | - Eka Roivainen
- Department of Exercise and Medical Physiology Verve, Oulu, Finland
| | - Juuso Nissilä
- Department of Biology, University of Oulu Oulu, Finland
| | - Arto J Hautala
- Department of Exercise and Medical Physiology Verve, Oulu, Finland
| | | | - Vesa J Kiviniemi
- Department of Diagnostic Radiology, University of Oulu Oulu, Finland
| | - Timo Takala
- Department of Sports and Exercise Medicine, Oulu Deaconess Institute Oulu, Finland
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Starck T, Nikkinen J, Rahko J, Remes J, Hurtig T, Haapsamo H, Jussila K, Kuusikko-Gauffin S, Mattila ML, Jansson-Verkasalo E, Pauls DL, Ebeling H, Moilanen I, Tervonen O, Kiviniemi VJ. Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing. Front Hum Neurosci 2013; 7:802. [PMID: 24319422 PMCID: PMC3837226 DOI: 10.3389/fnhum.2013.00802] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [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: 04/30/2013] [Accepted: 11/04/2013] [Indexed: 12/14/2022] Open
Abstract
In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.
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Affiliation(s)
- Tuomo Starck
- Department of Diagnostic Radiology, Oulu University Hospital Oulu, Finland ; Department of Diagnostic Radiology, Oulu University Oulu, Finland
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11
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Littow H, Elseoud AA, Haapea M, Isohanni M, Moilanen I, Mankinen K, Nikkinen J, Rahko J, Rantala H, Remes J, Starck T, Tervonen O, Veijola J, Beckmann C, Kiviniemi VJ. Age-Related Differences in Functional Nodes of the Brain Cortex - A High Model Order Group ICA Study. Front Syst Neurosci 2010; 4. [PMID: 20953235 PMCID: PMC2955419 DOI: 10.3389/fnsys.2010.00032] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [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: 02/05/2010] [Accepted: 06/18/2010] [Indexed: 12/03/2022] Open
Abstract
Functional MRI measured with blood oxygen dependent (BOLD) contrast in the absence of intermittent tasks reflects spontaneous activity of so-called resting state networks (RSN) of the brain. Group level independent component analysis (ICA) of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age-related effects from primary motor and sensory, and, higher level control RSNs. One hundred sixty-eight healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 years), 59 young adults (YA, 22.2 ± 0.6 years), and 54 older adults (OA, 42.7 ± 0.5 years), all with normal IQ. High model order group probabilistic ICA components (70) were calculated and dual-regression analysis was used to compare 21 RSN's spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN's reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs.
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Affiliation(s)
- Harri Littow
- Department of Diagnostic Radiology, Oulu University Hospital Oulu, Finland
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12
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Liu D, Yan C, Ren J, Yao L, Kiviniemi VJ, Zang Y. Using coherence to measure regional homogeneity of resting-state FMRI signal. Front Syst Neurosci 2010; 4:24. [PMID: 20589093 PMCID: PMC2893000 DOI: 10.3389/fnsys.2010.00024] [Citation(s) in RCA: 53] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2010] [Accepted: 05/23/2010] [Indexed: 11/13/2022] Open
Abstract
In this study, we applied coherence to voxel-wise measurement of regional homogeneity of resting-state functional magnetic resonance imaging (RS-fMRI) signal. We compared the current method, regional homogeneity based on coherence (Cohe-ReHo), with previously proposed method, ReHo based on Kendall's coefficient of concordance (KCC-ReHo), in terms of correlation and paired t-test in a large sample of healthy participants. We found the two measurements differed mainly in some brain regions where physiological noise is dominant. We also compared the sensitivity of these methods in detecting difference between resting-state conditions [eyes open (EO) vs. eyes closed (EC)] and in detecting abnormal local synchronization between two groups [attention deficit hyperactivity disorder (ADHD) patients vs. normal controls]. Our results indicated that Cohe-ReHo is more sensitive than KCC-ReHo to the difference between two conditions (EO vs. EC) as well as that between ADHD and normal controls. These preliminary results suggest that Cohe-ReHo is superior to KCC-ReHo. A possible reason is that coherence is not susceptible to random noise induced by phase delay among the time courses to be measured. However, further investigation is still needed to elucidate the sensitivity and specificity of these methods.
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Affiliation(s)
- Dongqiang Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China
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13
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Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kötter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SARB, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP. Toward discovery science of human brain function. Proc Natl Acad Sci U S A 2010; 107:4734-9. [PMID: 20176931 PMCID: PMC2842060 DOI: 10.1073/pnas.0911855107] [Citation(s) in RCA: 2100] [Impact Index Per Article: 150.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.
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Affiliation(s)
- Bharat B. Biswal
- Department of Radiology, New Jersey Medical School, Newark, NJ 07103
| | - Maarten Mennes
- Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, NYU Langone Medical Center, New York, NY 10016
| | - Xi-Nian Zuo
- Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, NYU Langone Medical Center, New York, NY 10016
| | - Suril Gohel
- Department of Radiology, New Jersey Medical School, Newark, NJ 07103
| | - Clare Kelly
- Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, NYU Langone Medical Center, New York, NY 10016
| | | | | | - Jonathan S. Adelstein
- Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, NYU Langone Medical Center, New York, NY 10016
| | - Randy L. Buckner
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138
| | - Stan Colcombe
- School of Psychology, University of Wales, Bangor, UK
| | - Anne-Marie Dogonowski
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Monique Ernst
- Mood and Anxiety Disorders Program, National Institute of Mental Health/National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892
| | - Damien Fair
- Behavioral Neuroscience Department, Oregon Health & Science University, Portland, OR 97239
| | - Michelle Hampson
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06511
| | - Matthew J. Hoptman
- Division of Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962
| | - James S. Hyde
- Biophysics Research Institute, Medical College of Wisconsin, Milwaukee, WI 53226
| | - Vesa J. Kiviniemi
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Rolf Kötter
- Donders Institute for Brain, Cognition, and Behavior, Center for Neuroscience, Radboud University Nijmegen Medical Center, 6500 HB Nijmegen, The Netherlands
| | - Shi-Jiang Li
- Biophysics Research Institute, Medical College of Wisconsin, Milwaukee, WI 53226
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang-Ming University, Taiwan
| | - Mark J. Lowe
- Imaging Institute, The Cleveland Clinic, Cleveland, OH 44195
| | - Clare Mackay
- FMRIB Centre, Oxford University, Oxford OX3 9DU, UK
| | - David J. Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710
| | - Kristoffer H. Madsen
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Daniel S. Margulies
- Department of Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Helen S. Mayberg
- Department of Psychiatry and Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322
| | - Katie McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | | | - Stewart H. Mostofsky
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205
| | - Bonnie J. Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239
| | - James J. Pekar
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205
| | - Scott J. Peltier
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI 48109
| | - Steven E. Petersen
- McDonnell Center for Higher Brain Functions, Washington University School of Medicine, St. Louis, MO 63110
| | - Valentin Riedl
- Departments of Neurology and Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, 81675 Munich, Germany
| | - Serge A. R. B. Rombouts
- Institute of Psychology and Department of Radiology, Leiden University Medical Center, Leiden University, Leiden, The Netherlands
| | - Bart Rypma
- Center for Brain Health and School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX 75080
| | - Bradley L. Schlaggar
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Sein Schmidt
- Department of Neurology, Charité Univesitaetsmedizin-Berlin, 10117 Berlin, Germany
| | - Rachael D. Seidler
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109
- School of Kinesiology, University of Michigan, Ann Arbor, MI 48109
| | - Greg J. Siegle
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213
| | - Christian Sorg
- Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, D-81675 Munich, Germany
| | - Gao-Jun Teng
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhong-Da Hospital, Southeast University, Nanjing 210009, China
| | - Juha Veijola
- Department of Psychiatry, Institute of Clinical Medicine and Department of Public Health Science, Institute of Health Science, University of Oulu, Oulu 90014, Finland
| | - Arno Villringer
- Department of Neurology, Charité Univesitaetsmedizin-Berlin, 10117 Berlin, Germany
- Berlin NeuroImaging Center, 10099 Berlin, Germany
| | - Martin Walter
- Department of Psychiatry, Otto-von-Guericke University of Magdeburg, Magdeburg 39106, Germany
| | - Lihong Wang
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710
| | - Xu-Chu Weng
- Laboratory for Higher Brain Function, Institute of Psychology, Chinese Academy of Sciences, Beijing 100864, China
| | - Susan Whitfield-Gabrieli
- Department of Brain and Cognitive Sciences, Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Boston, MA 02139
| | - Peter Williamson
- Department of Psychiatry, University of Western Ontario, London, ON N6A3H8, Canada
| | - Christian Windischberger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; and
| | - Yu-Feng Zang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Hong-Ying Zhang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhong-Da Hospital, Southeast University, Nanjing 210009, China
| | - F. Xavier Castellanos
- Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, NYU Langone Medical Center, New York, NY 10016
- Division of Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962
| | - Michael P. Milham
- Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, NYU Langone Medical Center, New York, NY 10016
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14
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Kiviniemi VJ, Starck T, Remes J, Long X, Nikkinen J, Haapea M, Veijola J, Moilanen I, Isohanni M, Zang YF, Tervonen O. Functional segmentation of the brain cortex using high model order group-PICA. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)72194-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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15
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Kiviniemi VJ, Haanpää H, Kantola JH, Jauhiainen J, Vainionpää V, Alahuhta S, Tervonen O. Midazolam sedation increases fluctuation and synchrony of the resting brain BOLD signal. Magn Reson Imaging 2005; 23:531-7. [PMID: 15919598 DOI: 10.1016/j.mri.2005.02.009] [Citation(s) in RCA: 120] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2004] [Accepted: 02/03/2005] [Indexed: 10/25/2022]
Abstract
The blood oxygen level-dependent (BOLD) magnetic resonance signal of functional brain cortices is dominated by very low frequency (VLF) fluctuations in anesthetized child patients. The temporal synchrony of the BOLD signal is also higher in anesthetized children compared with awake adults. The origin of the synchronous fluctuations can be related to maturation, pathological status or the anesthesia used in the imaging. Two of the three confounding variables (maturation and pathology) were controlled in this study. The effect of midazolam (4+/-0.8 mg) sedation on the BOLD signal was assessed in 12 healthy adults (aged 24+/-1.5 years) at 1.5 T. The VLF fluctuation power and temporal synchrony of the BOLD signal increased significantly after the sedation in the auditory and visual cortices. The fast Fourier transformation power spectral baseline fit parameters of the BOLD signal were also found to change significantly after sedation. It is concluded that the VLF fluctuation and temporal synchrony of the BOLD signal become increased after sedation in functional brain regions.
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Affiliation(s)
- Vesa J Kiviniemi
- Department of Diagnostic Radiology, Oula University Hospital, P.O. Box 50, OYS 90029, Finland.
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Kiviniemi VJ, Leppilahti J, Jalovaara P. Study of straight metatarsal osteotomy for the treatment of plantar callosities. Ann Chir Gynaecol 2001; 89:309-12. [PMID: 11204964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
BACKGROUND AND AIMS [corrected] To evaluate the outcome of transverse distal metatarsal osteotomies for intractable plantar callosity without hammer toe deformity and associated toe corns. MATERIAL AND METHODS Twenty-five plantar callosities were treated in 19 feet of 13 patients (mean age 48 years, 5 male, 8 female) with transverse distal metatarsal osteotomy. RESULTS Twenty-four of the osteotomies united primarily, one after revision. After a 7-year follow-up, 23 of the callosities had healed, two of them after an oblique reosteotomy. Eight hammer toe deformities had developed in the involved rays of four feet. Eight plantar callosities had developed outside the operated rays in five feet. Hallux valgus was a frequent finding in both operated and non-operated feet. CONCLUSION It seems that transverse distal metatarsal osteotomy is an effective treatment of intractable plantar callosities. Harmful hammer toe deformities and transfer lesions below adjacent metatarsal heads tend to develop over time.
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Affiliation(s)
- V J Kiviniemi
- Department of Diagnostic Radiology, Oulu University Hospital, Finland
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