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Bain KA, Kosik KB, Terada M, Gribble PA, Johnson NF. Contralateral thalamocortical connectivity is related to postural control in the uninvolved limb of older adults with history of ankle sprain. Gait Posture 2024; 109:115-119. [PMID: 38295486 DOI: 10.1016/j.gaitpost.2024.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Sensorimotor brain connectivity is often overlooked when determining relationships between postural control and motor performance following musculoskeletal injury. Thalamocortical brain connectivity is of particular interest as it represents the temporal synchrony of functionally and anatomically linked brain regions. Importantly, adults over the age of 60 are especially vulnerable to musculoskeletal injury due to age-related declines in postural control and brain connectivity. RESEARCH QUESTION Is there a relationship between thalamocortical connectivity and static postural control in older adults with a history of LAS? METHODS Data were analyzed from twenty older adults (mean age = 67.0 ± 4.3 yrs; 13 females) with a history of LAS. The sensorimotor network (SMN) was identified from resting-state MRI data, and a priori thalamic and postcentral gyri regions of interest were selected in order to determine left and right hemisphere thalamocortical connectivity. Balance was assessed for the involved and non-involved limbs via center of pressure velocity (COPV) in the medial-lateral (ML) and anterior-posterior (AP) directions. RESULTS Contralateral thalamocortical connectivity was significantly associated with COPV_ML COPV_ML (r = -0.474, P = 0.05) and COPV_AP (r = -0.622, P = 0.008) in the non-involved limb. No significant association was observed between involved limb balance and contralateral thalamocortical connectivity (COPV_ML: r = -0.08, P = 0.77; COPV_AP: r = 0.12, P = 0.63). SIGNIFICANCE A significant relationship between thalamocortical connectivity and static postural control was observed in the non-involved, but not the involved limb in older adults with a history of LAS. Findings suggest that thalamocortical connectivity may lead to or be the product of LAS.
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Affiliation(s)
- Katherine A Bain
- Division of Physical Therapy, Shenandoah University, Leesburg, VA, USA.
| | - Kyle B Kosik
- Department of Athletic Training & Clinical Nutrition, University of Kentucky, Lexington, KY, USA
| | - Masafumi Terada
- Faculty of Sport and Health Science, Ritusmeikan University, Kusatusu, Shiga-ken, Japan
| | - Phillip A Gribble
- Department of Athletic Training & Clinical Nutrition, University of Kentucky, Lexington, KY, USA
| | - Nathan F Johnson
- Department of Physical Therapy, University of Kentucky, Lexington, KY, USA
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Chen Y, Zhao W, Yi S, Liu J. The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis. Front Neurosci 2023; 17:1174080. [PMID: 37811326 PMCID: PMC10559726 DOI: 10.3389/fnins.2023.1174080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/11/2023] [Indexed: 10/10/2023] Open
Abstract
Objective Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD. Methods English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity. Results Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity. Conclusion ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
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Corrêa DG, van Duinkerken E, Farinhas JGD, Pereira VC, Gasparetto EL, Alves-Leon SV, Lopes FCR. Influence of natalizumab on resting-state connectivity in patients with multiple sclerosis. J Cent Nerv Syst Dis 2023; 15:11795735231195775. [PMID: 37600237 PMCID: PMC10433731 DOI: 10.1177/11795735231195775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023] Open
Abstract
Background Changes in brain connectivity occur in patients with multiple sclerosis (MS), even in patients under disease-modifying therapies. Using magnetic resonance imaging (MRI) to asses patients treated with disease-modifying therapies, such as natalizumab, can elucidate the mechanisms involved in clinical deterioration in MS. Objectives To evaluate differences in resting-state functional connectivity among MS patients treated with natalizumab, MS patients not treated with natalizumab, and controls. Design Single-center retrospective cross-sectional study. Methods Twenty-three MS patients being treated with natalizumab were retrospectively compared with 23 MS patients who were naïve for natalizumab, and were using first-line medications (interferon-β and/or glatiramer acetate), and 17 gender- and age-matched control subjects. The MS patient groups were also matched for time since diagnosis and hyperintense lesion volume on FLAIR. All participants underwent brain MRI using a 3 Tesla scanner. Independent component analysis and dual regression were used to identify resting-state functional connectivity using the FMRIB Software Library. Results In comparison to controls, the MS patients treated with natalizumab presented decreased connectivity in the left orbitofrontal cortex, in the anterior cingulate and orbitofrontal cortex network. The patients not treated with natalizumab presented increased connectivity in the secondary visual, sensorimotor, and ventral attention networks in comparison to controls.Compared to patients treated with natalizumab, the patients not using natalizumab presented increased connectivity in the left Heschl's gyrus and in the right superior frontal gyrus in the ventral attention network. Conclusion Differences in brain connectivity between MS patients not treated with natalizumab, healthy controls, and patients treated with natalizumab may be secondary to suboptimal neuronal compensation due to prior less efficient treatments, or due to a compensation in response to maladaptive plasticity.
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Affiliation(s)
- Diogo G. Corrêa
- Department of Radiology, Clínica de Diagnóstico por Imagem (CDPI)/DASA, Barra da Tijuca, Brazil
- Department of Radiology, Federal Fluminense University, Niterói, Brazil
| | - Eelco van Duinkerken
- Department of Medical Psychology, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
- Post-Graduate Program in Neurology, Hospital Universitário Gaffrée e Guinle, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - João Gabriel D. Farinhas
- Post-Graduate Program in Neurology, Hospital Universitário Gaffrée e Guinle, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Neurology, Hospital Universitário Clementino Fraga Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Valéria C. Pereira
- Department of Neurology, Hospital Universitário Clementino Fraga Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Emerson L. Gasparetto
- Department of Radiology, Clínica de Diagnóstico por Imagem (CDPI)/DASA, Barra da Tijuca, Brazil
| | - Soniza V. Alves-Leon
- Post-Graduate Program in Neurology, Hospital Universitário Gaffrée e Guinle, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Neurology, Hospital Universitário Clementino Fraga Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernanda Cristina R. Lopes
- Department of Radiology, Clínica de Diagnóstico por Imagem (CDPI)/DASA, Barra da Tijuca, Brazil
- Department of Radiology, Federal Fluminense University, Niterói, Brazil
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English BA, Ereshefsky L. Experimental Medicine Approaches in Early-Phase CNS Drug Development. ADVANCES IN NEUROBIOLOGY 2023; 30:417-455. [PMID: 36928860 DOI: 10.1007/978-3-031-21054-9_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
Traditionally, Phase 1 clinical trials were largely conducted in healthy normal volunteers and focused on collection of safety, tolerability, and pharmacokinetic data. However, in the CNS therapeutic area, with more drugs failing in later phase development, Phase 1 trials have undergone an evolution that includes incorporation of novel approaches involving novel study designs, inclusion of biomarkers, and early inclusion of patients to improve the pharmacologic understanding of novel CNS-active compounds early in clinical development with the hope of improving success in later phase pivotal trials. In this chapter, the authors will discuss the changing landscape of Phase 1 clinical trials in CNS, including novel trial methodology, inclusion of pharmacodynamic biomarkers, and experimental medicine approaches to inform early decision-making in clinical development.
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Zhang Q, Li B, Jin S, Liu W, Liu J, Xie S, Zhang L, Kang Y, Ding Y, Zhang X, Cheng W, Yang Z. Comparing the Effectiveness of Brain Structural Imaging, Resting-state fMRI, and Naturalistic fMRI in Recognizing Social Anxiety Disorder in Children and Adolescents. Psychiatry Res Neuroimaging 2022; 323:111485. [PMID: 35567906 DOI: 10.1016/j.pscychresns.2022.111485] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/07/2022] [Accepted: 04/16/2022] [Indexed: 01/11/2023]
Abstract
Social anxiety disorder (SAD) is a common anxiety disorder in childhood and adolescence. Studies on SAD in adults have reported both structural and functional aberrancies of the brain at the group level. However, evidence has shown differences in anxiety-related brain abnormalities between adolescents and adults. Since children and adolescents can afford limited scan time, optimizing the scan tasks is essential for SAD research in children and adolescents. Thus, we need to address whether brain structure, resting-state fMRI, and naturalistic imaging enable individualized identification of SAD in children and adolescents, which measurement is more effective, and whether pooling multi-modal features can improve the identification of SAD. We comprehensively addressed these questions by building machine learning models based on parcel-wise brain features. We found that naturalistic fMRI yielded higher classification accuracy (69.17%) than the other modalities and the classification performance showed dependence on the contents of the movie. The classification models also identified contributing brain regions, some of which exhibited correlations with the symptoms scores of SAD. However, pooling brain features from the three modalities did not help enhance the classification accuracy. These results support the application of carefully designed naturalistic imaging in recognizing children and adolescents at risk of SAD.
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Affiliation(s)
- Qinjian Zhang
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Baobin Li
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Shuyu Jin
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjing Liu
- Department of Child and Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingjing Liu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuqi Xie
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Zhang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinzhi Kang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Ding
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaochen Zhang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenhong Cheng
- Department of Child and Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Zhi Yang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.
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6
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Haraguchi R, Hoshi H, Ichikawa S, Hanyu M, Nakamura K, Fukasawa K, Poza J, Rodríguez-González V, Gómez C, Shigihara Y. The Menstrual Cycle Alters Resting-State Cortical Activity: A Magnetoencephalography Study. Front Hum Neurosci 2021; 15:652789. [PMID: 34381340 PMCID: PMC8350571 DOI: 10.3389/fnhum.2021.652789] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/28/2021] [Indexed: 11/24/2022] Open
Abstract
Resting-state neural oscillations are used as biomarkers for functional diseases such as dementia, epilepsy, and stroke. However, accurate interpretation of clinical outcomes requires the identification and minimisation of potential confounding factors. While several studies have indicated that the menstrual cycle also alters brain activity, most of these studies were based on visual inspection rather than objective quantitative measures. In the present study, we aimed to clarify the effect of the menstrual cycle on spontaneous neural oscillations based on quantitative magnetoencephalography (MEG) parameters. Resting-state MEG activity was recorded from 25 healthy women with normal menstrual cycles. For each woman, resting-state brain activity was acquired twice using MEG: once during their menstrual period (MP) and once outside of this period (OP). Our results indicated that the median frequency and peak alpha frequency of the power spectrum were low, whereas Shannon spectral entropy was high, during the MP. Theta intensity within the right temporal cortex and right limbic system was significantly lower during the MP than during the OP. High gamma intensity in the left parietal cortex was also significantly lower during the MP than during the OP. Similar differences were also observed in the parietal and occipital regions between the proliferative (the late part of the follicular phase) and secretory phases (luteal phase). Our findings suggest that the menstrual cycle should be considered to ensure accurate interpretation of functional neuroimaging in clinical practice.
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Affiliation(s)
- Rika Haraguchi
- Clinical Laboratory, Kumagaya General Hospital, Kumagaya, Japan
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | - Sayuri Ichikawa
- Clinical Laboratory, Kumagaya General Hospital, Kumagaya, Japan
| | - Mayuko Hanyu
- Department of Gynaecology, Kumagaya General Hospital, Kumagaya, Japan
| | - Kohei Nakamura
- Department of Gynaecology, Kumagaya General Hospital, Kumagaya, Japan.,Genomics Unit, Keio Cancer Centre, Keio University School of Medicine, Minato, Japan
| | | | - Jesús Poza
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain.,Instituto de Investigación en Matemáticas (IMUVA), University of Valladolid, Valladolid, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Yoshihito Shigihara
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan.,Precision Medicine Centre, Kumagaya General Hospital, Kumagaya, Japan
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7
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Brahimaj BC, Kochanski RB, Pearce JJ, Guryildirim M, Gerard CS, Kocak M, Sani S, Byrne RW. Structural and Functional Imaging in Glioma Management. Neurosurgery 2021; 88:211-221. [PMID: 33313852 DOI: 10.1093/neuros/nyaa360] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 04/26/2020] [Indexed: 01/08/2023] Open
Abstract
The goal of glioma surgery is maximal safe resection in order to provide optimal tumor control and survival benefit to the patient. There are multiple imaging modalities beyond traditional contrast-enhanced magnetic resonance imaging (MRI) that have been incorporated into the preoperative workup of patients presenting with gliomas. The aim of these imaging modalities is to identify cortical and subcortical areas of eloquence, and their relationship to the lesion. In this article, multiple modalities are described with an emphasis on the underlying technology, clinical utilization, advantages, and disadvantages of each. functional MRI and its role in identifying hemispheric dominance and areas of language and motor are discussed. The nuances of magnetoencephalography and transcranial magnetic stimulation in localization of eloquent cortex are examined, as well as the role of diffusion tensor imaging in defining normal white matter tracts in glioma surgery. Lastly, we highlight the role of stimulated Raman spectroscopy in intraoperative histopathological diagnosis of tissue to guide tumor resection. Tumors may shift the normal arrangement of functional anatomy in the brain; thus, utilization of multiple modalities may be helpful in operative planning and patient counseling for successful surgery.
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Affiliation(s)
- Bledi C Brahimaj
- Department of Neurosurgery, Rush University Medical Center, Chicago, Illinois
| | - Ryan B Kochanski
- Department of Neurosurgery, Rush University Medical Center, Chicago, Illinois
| | - John J Pearce
- Department of Neurosurgery, Rush University Medical Center, Chicago, Illinois
| | - Melike Guryildirim
- Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland
| | - Carter S Gerard
- Swedish Neuroscience Institute, Swedish Medical Center, Seattle, Washington
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois
| | - Sepehr Sani
- Department of Neurosurgery, Rush University Medical Center, Chicago, Illinois
| | - Richard W Byrne
- Department of Neurosurgery, Rush University Medical Center, Chicago, Illinois
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8
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Perez DL, Nicholson TR, Asadi-Pooya AA, Bègue I, Butler M, Carson AJ, David AS, Deeley Q, Diez I, Edwards MJ, Espay AJ, Gelauff JM, Hallett M, Horovitz SG, Jungilligens J, Kanaan RAA, Tijssen MAJ, Kozlowska K, LaFaver K, LaFrance WC, Lidstone SC, Marapin RS, Maurer CW, Modirrousta M, Reinders AATS, Sojka P, Staab JP, Stone J, Szaflarski JP, Aybek S. Neuroimaging in Functional Neurological Disorder: State of the Field and Research Agenda. Neuroimage Clin 2021; 30:102623. [PMID: 34215138 PMCID: PMC8111317 DOI: 10.1016/j.nicl.2021.102623] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 03/03/2021] [Indexed: 02/06/2023]
Abstract
Functional neurological disorder (FND) was of great interest to early clinical neuroscience leaders. During the 20th century, neurology and psychiatry grew apart - leaving FND a borderland condition. Fortunately, a renaissance has occurred in the last two decades, fostered by increased recognition that FND is prevalent and diagnosed using "rule-in" examination signs. The parallel use of scientific tools to bridge brain structure - function relationships has helped refine an integrated biopsychosocial framework through which to conceptualize FND. In particular, a growing number of quality neuroimaging studies using a variety of methodologies have shed light on the emerging pathophysiology of FND. This renewed scientific interest has occurred in parallel with enhanced interdisciplinary collaborations, as illustrated by new care models combining psychological and physical therapies and the creation of a new multidisciplinary FND society supporting knowledge dissemination in the field. Within this context, this article summarizes the output of the first International FND Neuroimaging Workgroup meeting, held virtually, on June 17th, 2020 to appraise the state of neuroimaging research in the field and to catalyze large-scale collaborations. We first briefly summarize neural circuit models of FND, and then detail the research approaches used to date in FND within core content areas: cohort characterization; control group considerations; task-based functional neuroimaging; resting-state networks; structural neuroimaging; biomarkers of symptom severity and risk of illness; and predictors of treatment response and prognosis. Lastly, we outline a neuroimaging-focused research agenda to elucidate the pathophysiology of FND and aid the development of novel biologically and psychologically-informed treatments.
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Affiliation(s)
- David L Perez
- Departments of Neurology and Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Timothy R Nicholson
- Section of Cognitive Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ali A Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz Iran; Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Indrit Bègue
- Division of Adult Psychiatry, Department of Psychiatry, University of Geneva, Geneva Switzerland; Service of Neurology Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland
| | - Matthew Butler
- Section of Cognitive Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alan J Carson
- Centre for Clinical Brain Sciences, The University of Edinburgh, EH16 4SB, UK
| | - Anthony S David
- Institute of Mental Health, University College London, London, UK
| | - Quinton Deeley
- South London and Maudsley NHS Foundation Trust, London UK Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Ibai Diez
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mark J Edwards
- Neurosciences Research Centre, St George's University of London, London, UK
| | - Alberto J Espay
- James J. and Joan A. Gardner Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, USA
| | - Jeannette M Gelauff
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, Netherlands
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Silvina G Horovitz
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Johannes Jungilligens
- Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum, Germany
| | - Richard A A Kanaan
- Department of Psychiatry, University of Melbourne, Austin Health Heidelberg, Australia
| | - Marina A J Tijssen
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Groningen, University of Groningen, The Netherlands
| | - Kasia Kozlowska
- The Children's Hospital at Westmead, Westmead Institute of Medical Research, University of Sydney Medical School, Sydney, NSW, Australia
| | - Kathrin LaFaver
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - W Curt LaFrance
- Departments of Psychiatry and Neurology, Rhode Island Hospital, Brown University, Providence, RI, USA
| | - Sarah C Lidstone
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, University Health Network and the University of Toronto, Toronto, Ontario, Canada
| | - Ramesh S Marapin
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Groningen, University of Groningen, The Netherlands
| | - Carine W Maurer
- Department of Neurology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA
| | - Mandana Modirrousta
- Department of Psychiatry, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Antje A T S Reinders
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Petr Sojka
- Department of Psychiatry, University Hospital Brno, Czech Republic
| | - Jeffrey P Staab
- Departments of Psychiatry and Psychology and Otorhinolaryngology-Head and Neck Surgery, Mayo Clinic Rochester, MN, USA
| | - Jon Stone
- Centre for Clinical Brain Sciences, The University of Edinburgh, EH16 4SB, UK
| | - Jerzy P Szaflarski
- University of Alabama at Birmingham Epilepsy Center, Department of Neurology, University of Alabama at Birmingham Birmingham, AL, USA
| | - Selma Aybek
- Neurology Department, Psychosomatic Medicine Unit, Bern University Hospital Inselspital, University of Bern, Bern, Switzerland
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9
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Gaillard A, Fehring DJ, Rossell SL. Sex differences in executive control: A systematic review of functional neuroimaging studies. Eur J Neurosci 2021; 53:2592-2611. [PMID: 33423339 DOI: 10.1111/ejn.15107] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/22/2020] [Accepted: 01/01/2021] [Indexed: 01/21/2023]
Abstract
The number of studies investigating sex differences in executive functions, particularly those using human functional neuroimaging techniques, has risen dramatically in the past decade. However, the influences of sex on executive function are still underexplored and poorly characterized. To address this, we conducted a systematic literature review of functional neuroimaging studies investigating sex differences in three prominent executive control domains of cognitive set-shifting, performance monitoring, and response inhibition. PubMed, Web of Science, and Scopus were systematically searched. Following the application of exclusion criteria, 21 studies were included, with a total of 677 females and 686 males. Ten of these studies were fMRI and PET, eight were EEG, and three were NIRS. At present, there is evidence for sex differences in the neural networks underlying all tasks of executive control included in this review suggesting males and females engage different strategies depending on task demands. There was one task exception, the 2-Back task, which showed no sex differences. Due to methodological variability and the involvement of multiple neural networks, a simple overarching statement with regard to gender differences during executive control cannot be provided. As such, we discuss limitations within the current literature and methodological considerations that should be employed in future research. Importantly, sex differences in neural mechanisms are present in the majority of tasks assessed, and thus should not be ignored in future research. PROSPERO registration information: CRD42019124772.
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Affiliation(s)
- Alexandra Gaillard
- Centre for Mental Health, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC., Australia
| | - Daniel J Fehring
- Cognitive Neuroscience Laboratory, Monash Biomedicine Discovery Institute, Department of Physiology, Monash University, Clayton, VIC., Australia.,ARC Centre of Excellence in Integrative Brain Function, Monash University, Clayton, VIC., Australia
| | - Susan L Rossell
- Centre for Mental Health, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC., Australia.,Psychiatry, St Vincent's Hospital, Melbourne, VIC., Australia
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Hoshi H, Shigihara Y. Age- and gender-specific characteristics of the resting-state brain activity: a magnetoencephalography study. Aging (Albany NY) 2020; 12:21613-21637. [PMID: 33147568 PMCID: PMC7695396 DOI: 10.18632/aging.103956] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 08/01/2020] [Indexed: 12/29/2022]
Abstract
Aging and gender influence regional brain activities. Although these biases should be considered during the clinical examinations using magnetoencephalography, they have yet to be standardized. In the present study, resting-state magnetoencephalography data were recorded from 54 healthy females and 48 males aged 22 to 75 years, who were controlled for cognitive performance. The regional oscillatory power was estimated for each frequency band (delta, theta, alpha, beta, low-gamma, and high-gamma) using the sLORETA-like algorithm and the biases of age and gender were evaluated, respectively. The results showed that faster oscillatory powers increased with age in the rostral regions and decreased in the caudal regions, while few slower oscillatory powers changed with age. Gender differences in oscillatory powers were found in a broad frequency range, mostly in the caudal brain regions. The present study characterized the effects of healthy aging and gender asymmetricity on the regional resting-state brain activity, with the aim to facilitate the accurate and efficient use of magnetoencephalography in clinical practice.
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Affiliation(s)
- Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro-shi, Hokkaido, Japan
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11
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Hacker CD, Roland JL, Kim AH, Shimony JS, Leuthardt EC. Resting-state network mapping in neurosurgical practice: a review. Neurosurg Focus 2019; 47:E15. [PMID: 31786561 PMCID: PMC9841914 DOI: 10.3171/2019.9.focus19656] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 09/12/2019] [Indexed: 01/18/2023]
Abstract
Resting-state functional MRI (rs-fMRI) is a well-established method for studying intrinsic connectivity and mapping the topography of functional networks in the human brain. In the clinical setting, rs-fMRI has been used to define functional topography, typically language and motor systems, in the context of preoperative planning for neurosurgery. Intraoperative mapping of critical speech and motor areas with electrocortical stimulation (ECS) remains standard practice, but preoperative noninvasive mapping has the potential to reduce operative time and provide functional localization when awake mapping is not feasible. Task-based fMRI has historically been used for this purpose, but it can be limited by the young age of the patient, cognitive impairment, poor cooperation, and need for sedation. Resting-state fMRI allows reliable analysis of all functional networks with a single study and is inherently independent of factors affecting task performance. In this review, the authors provide a summary of the theory and methods for resting-state network mapping. They provide case examples illustrating clinical implementation and discuss limitations of rs-fMRI and review available data regarding performance in comparison to ECS. Finally, they discuss novel opportunities for future clinical applications and prospects for rs-fMRI beyond mapping of regions to avoid during surgery but, instead, as a tool to guide novel network-based therapies.
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Affiliation(s)
- Carl D. Hacker
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Jarod L. Roland
- Department of Neurosurgery, University of California, San Francisco, California
| | - Albert H. Kim
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric C. Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
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12
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Li X, Guo N, Li Q. Functional Neuroimaging in the New Era of Big Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2019; 17:393-401. [PMID: 31809864 PMCID: PMC6943787 DOI: 10.1016/j.gpb.2018.11.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 09/17/2018] [Accepted: 12/25/2018] [Indexed: 12/15/2022]
Abstract
The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.
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Affiliation(s)
- Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ning Guo
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Quanzheng Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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13
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Roland JL, Griffin N, Hacker CD, Vellimana AK, Akbari SH, Shimony JS, Smyth MD, Leuthardt EC, Limbrick DD. Resting-state functional magnetic resonance imaging for surgical planning in pediatric patients: a preliminary experience. J Neurosurg Pediatr 2017; 20:583-590. [PMID: 28960172 PMCID: PMC5952608 DOI: 10.3171/2017.6.peds1711] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Cerebral mapping for surgical planning and operative guidance is a challenging task in neurosurgery. Pediatric patients are often poor candidates for many modern mapping techniques because of inability to cooperate due to their immature age, cognitive deficits, or other factors. Resting-state functional MRI (rs-fMRI) is uniquely suited to benefit pediatric patients because it is inherently noninvasive and does not require task performance or significant cooperation. Recent advances in the field have made mapping cerebral networks possible on an individual basis for use in clinical decision making. The authors present their initial experience translating rs-fMRI into clinical practice for surgical planning in pediatric patients. METHODS The authors retrospectively reviewed cases in which the rs-fMRI analysis technique was used prior to craniotomy in pediatric patients undergoing surgery in their institution. Resting-state analysis was performed using a previously trained machine-learning algorithm for identification of resting-state networks on an individual basis. Network maps were uploaded to the clinical imaging and surgical navigation systems. Patient demographic and clinical characteristics, including need for sedation during imaging and use of task-based fMRI, were also recorded. RESULTS Twenty patients underwent rs-fMRI prior to craniotomy between December 2013 and June 2016. Their ages ranged from 1.9 to 18.4 years, and 12 were male. Five of the 20 patients also underwent task-based fMRI and one underwent awake craniotomy. Six patients required sedation to tolerate MRI acquisition, including resting-state sequences. Exemplar cases are presented including anatomical and resting-state functional imaging. CONCLUSIONS Resting-state fMRI is a rapidly advancing field of study allowing for whole brain analysis by a noninvasive modality. It is applicable to a wide range of patients and effective even under general anesthesia. The nature of resting-state analysis precludes any need for task cooperation. These features make rs-fMRI an ideal technology for cerebral mapping in pediatric neurosurgical patients. This review of the use of rs-fMRI mapping in an initial pediatric case series demonstrates the feasibility of utilizing this technique in pediatric neurosurgical patients. The preliminary experience presented here is a first step in translating this technique to a broader clinical practice.
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Affiliation(s)
- Jarod L. Roland
- Department of Neurological Surgery, Washington University in St. Louis, Missouri
| | - Natalie Griffin
- Department of School of Medicine, Washington University in St. Louis, Missouri
| | - Carl D. Hacker
- Department of Neurological Surgery, Washington University in St. Louis, Missouri
| | - Ananth K. Vellimana
- Department of Neurological Surgery, Washington University in St. Louis, Missouri
| | - S. Hassan Akbari
- Department of Neurological Surgery, Washington University in St. Louis, Missouri
| | - Joshua S. Shimony
- Department of Radiology, Washington University in St. Louis, Missouri
| | - Matthew D. Smyth
- Department of Neurological Surgery, Washington University in St. Louis, Missouri
- Department of Pediatrics, Washington University in St. Louis, Missouri
| | - Eric C. Leuthardt
- Department of Neurological Surgery, Washington University in St. Louis, Missouri
- Department of Neuroscience, Washington University in St. Louis, Missouri
- Department of Biomedical Engineering, Washington University in St. Louis, Missouri
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri
- Center for Innovation in Neuroscience and Technology, Washington University in St. Louis, Missouri
- Brain Laser Center, Washington University in St. Louis, Missouri
| | - David D. Limbrick
- Department of Neurological Surgery, Washington University in St. Louis, Missouri
- Department of Pediatrics, Washington University in St. Louis, Missouri
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Gonzalez-Castillo J, Chen G, Nichols TE, Bandettini PA. Variance decomposition for single-subject task-based fMRI activity estimates across many sessions. Neuroimage 2016; 154:206-218. [PMID: 27773827 DOI: 10.1016/j.neuroimage.2016.10.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 10/07/2016] [Accepted: 10/14/2016] [Indexed: 12/29/2022] Open
Abstract
Here we report an exploratory within-subject variance decomposition analysis conducted on a task-based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within-subject variance was segregated into four primary components: variance across-sessions, variance across-runs within a session, variance across-blocks within a run, and residual measurement/modeling error. Our results reveal inhomogeneous and distinct spatial distributions of these variance components across significantly active voxels in grey matter. Measurement error is dominant across the whole brain. Detailed evaluation of the remaining three components shows that across-session variance is the second largest contributor to total variance in occipital cortex, while across-runs variance is the second dominant source for the rest of the brain. Network-specific analysis revealed that across-block variance contributes more to total variance in higher-order cognitive networks than in somatosensory cortex. Moreover, in some higher-order cognitive networks across-block variance can exceed across-session variance. These results help us better understand the temporal (i.e., across blocks, runs and sessions) and spatial distributions (i.e., across different networks) of within-subject natural variability in estimates of task responses in fMRI. They also suggest that different brain regions will show different natural levels of test-retest reliability even in the absence of residual artifacts and sufficiently high contrast-to-noise measurements. Further confirmation with a larger sample of subjects and other tasks is necessary to ensure generality of these results.
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Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, National Institutes of Health, Bethesda, MD, United States
| | - Thomas E Nichols
- Department of Statistics & WMG, University of Warwick, Coventry, UK
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States; Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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15
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Silveri MM, Dager AD, Cohen-Gilbert JE, Sneider JT. Neurobiological signatures associated with alcohol and drug use in the human adolescent brain. Neurosci Biobehav Rev 2016; 70:244-259. [PMID: 27377691 DOI: 10.1016/j.neubiorev.2016.06.042] [Citation(s) in RCA: 86] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 06/17/2016] [Accepted: 06/30/2016] [Indexed: 01/02/2023]
Abstract
Magnetic resonance (MR) techniques provide opportunities to non-invasively characterize neurobiological milestones of adolescent brain development. Juxtaposed to the critical finalization of brain development is initiation of alcohol and substance use, and increased frequency and quantity of use, patterns that can lead to abuse and addiction. This review provides a comprehensive overview of existing MR studies of adolescent alcohol and drug users. The most common alterations reported across substance used and MR modalities are in the frontal lobe (63% of published studies). This is not surprising, given that this is the last region to reach neurobiological adulthood. Comparatively, evidence is less consistent regarding alterations in regions that mature earlier (e.g., amygdala, hippocampus), however newer techniques now permit investigations beyond regional approaches that are uncovering network-level vulnerabilities. Regardless of whether neurobiological signatures exist prior to the initiation of use, this body of work provides important direction for ongoing prospective investigations of adolescent brain development, and the significant impact of alcohol and substance use on the brain during the second decade of life.
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Affiliation(s)
- Marisa M Silveri
- Neurodevelopmental Laboratory on Addictions and Mental Health, McLean Hospital, Belmont, MA, USA; McLean Imaging Center, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Alecia D Dager
- Olin Neuropsychiatry Research Center, Hartford, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Julia E Cohen-Gilbert
- Neurodevelopmental Laboratory on Addictions and Mental Health, McLean Hospital, Belmont, MA, USA; McLean Imaging Center, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Jennifer T Sneider
- Neurodevelopmental Laboratory on Addictions and Mental Health, McLean Hospital, Belmont, MA, USA; McLean Imaging Center, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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16
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[Functional brain imaging]. Radiologe 2016; 56:148-58. [PMID: 26767522 DOI: 10.1007/s00117-015-0072-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
METHOD Functional magnetic resonance imaging (fMRI) is a non-invasive method that has become one of the major tools for understanding human brain function and in recent years has also been developed for clinical applications. PERFORMANCE Changes in hemodynamic signals correspond to changes in neuronal activity with good spatial and temporal resolution in fMRI. Using high-field MR systems and increasingly dedicated statistics and postprocessing, activated brain areas can be detected and superimposed on anatomical images. Currently, fMRI data are often combined in multimodal imaging, e. g. with diffusion tensor imaging (DTI) sequences. This method is helping to further understand the physiology of cognitive brain processes and is also being used in a number of clinical applications. In addition to the blood oxygenation level-dependent (BOLD) signals, this article deals with the construction of fMRI investigations, selection of paradigms and evaluation in the clinical routine. Clinically, this method is mainly used in the planning of brain surgery, analyzing the location of brain tumors in relation to eloquent brain areas and the lateralization of language processing. PRACTICAL RECOMMENDATIONS As the BOLD signal is dependent on the strength of the magnetic field as well as other limitations, an overview of recent developments is given. Increases of magnetic field strength (7 T), available head coils and advances in MRI analytical methods have led to constant improvement in fMRI signals and experimental design. Especially the depiction of eloquent brain regions can be done easily and quickly and has become an essential part of presurgical planning.
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Affective mentalizing and brain activity at rest in the behavioral variant of frontotemporal dementia. NEUROIMAGE-CLINICAL 2015; 9:484-97. [PMID: 26594631 PMCID: PMC4600858 DOI: 10.1016/j.nicl.2015.08.012] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 08/18/2015] [Accepted: 08/19/2015] [Indexed: 12/13/2022]
Abstract
Background bvFTD patients display an impairment in the attribution of cognitive and affective states to others, reflecting GM atrophy in brain regions associated with social cognition, such as amygdala, superior temporal cortex and posterior insula. Distinctive patterns of abnormal brain functioning at rest have been reported in bvFTD, but their relationship with defective attribution of affective states has not been investigated. Objective To investigate the relationship among resting-state brain activity, gray matter (GM) atrophy and the attribution of mental states in the behavioral variant of fronto-temporal degeneration (bvFTD). Methods We compared 12 bvFTD patients with 30 age- and education-matched healthy controls on a) performance in a task requiring the attribution of affective vs. cognitive mental states; b) metrics of resting-state activity in known functional networks; and c) the relationship between task-performances and resting-state metrics. In addition, we assessed a connection between abnormal resting-state metrics and GM atrophy. Results Compared with controls, bvFTD patients showed a reduction of intra-network coherent activity in several components, as well as decreased strength of activation in networks related to attentional processing. Anomalous resting-state activity involved networks which also displayed a significant reduction of GM density. In patients, compared with controls, higher affective mentalizing performance correlated with stronger functional connectivity between medial prefrontal sectors of the default-mode and attentional/performance monitoring networks, as well as with increased coherent activity in components of the executive, sensorimotor and fronto-limbic networks. Conclusions Some of the observed effects may reflect specific compensatory mechanisms for the atrophic changes involving regions in charge of affective mentalizing. The analysis of specific resting-state networks thus highlights an intermediate level of analysis between abnormal brain structure and impaired behavioral performance in bvFTD, reflecting both dysfunction and compensation mechanisms. bvFTD patients are impaired in the attribution of mental states to others (theory of mind, ToM). bvFTD patients' ToM deficit involves mainly the attribution of affective states. Affective ToM deficits in bvFTD reflect gray matter atrophy in frontolimbic areas. Affective ToM deficits in bvFTD reflect altered frontomedial resting-state activity. Brain activity at rest reflects both dysfunction and compensation mechanisms in bvFTD.
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Key Words
- AD, Alzheimer's disease
- Affective mentalizing
- BOLD, blood-oxygen-level-dependent
- Behavioral variant of frontotemporal dementia
- CI, causal inferences
- Default mode network
- EA, emotion attribution
- Executive functioning network
- FDR, false discovery rate
- FTLD, frontotemporal lobar degeneration
- GM, gray matter
- IA, intention attribution
- MANCOVAN, multivariate analysis of covariance
- MMSE, Mini-Mental State Examination
- PCA, principal component analysis
- RSNs, resting-state networks
- Resting state functional MRI
- SET, story-based empathy task
- SPM, statistical parametric mapping
- ToM, theory of mind
- VBM, voxel based morphometry
- aDMN, anterior default mode network
- bvFTD, behavioral variant of frontotemporal dementia
- gICA, group independent component analysis
- pDMN, posterior default mode network
- rs-fMRI, resting-state fMRI
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