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G-Santoyo I, Ramírez-Carrillo E, Sanchez JD, López-Corona O. Potential long consequences from internal and external ecology: loss of gut microbiota antifragility in children from an industrialized population compared with an indigenous rural lifestyle. J Dev Orig Health Dis 2023; 14:469-480. [PMID: 37222148 DOI: 10.1017/s2040174423000144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Human health is strongly mediated by the gut microbiota ecosystem, which, in turn, depends not only on its state but also on its dynamics and how it responds to perturbations. Healthy microbiota ecosystems tend to be in criticality and antifragile dynamics corresponding to a maximum complexity configuration, which may be assessed with information and network theory analysis. Under this complex system perspective, we used a new analysis of published data to show that a children's population with an industrialized urban lifestyle from Mexico City exhibits informational and network characteristics similar to parasitized children from a rural indigenous population in the remote mountainous region of Guerrero, México. We propose then, that in this critical age for gut microbiota maturation, the industrialized urban lifestyle could be thought of as an external perturbation to the gut microbiota ecosystem, and we show that it produces a similar loss in criticality/antifragility as the one observed by internal perturbation due to parasitosis by the helminth A. lumbricoides. Finally, several general complexity-based guidelines to prevent or restore gut ecosystem antifragility are discussed.
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Affiliation(s)
- Isaac G-Santoyo
- Neuroecology Lab, Department of Psychology, UNAM, México, 04510
- Unidad de Investigación en Psicobiología y Neurociencias, Department of Psychology, UNAM, México, 04510
| | | | | | - Oliver López-Corona
- Investigadores por México (IxM)-CONACyT, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), UNAM, México, 04510
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2
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Ramírez-Carrillo E, G-Santoyo I, López-Corona O, Rojas-Ramos OA, Falcón LI, Gaona O, de la Fuente Rodríguez RM, Hernández Castillo A, Cerqueda-García D, Sánchez-Quinto A, Hernández-Muciño D, Nieto J. Similar connectivity of gut microbiota and brain activity networks is mediated by animal protein and lipid intake in children from a Mexican indigenous population. PLoS One 2023; 18:e0281385. [PMID: 37384745 PMCID: PMC10310019 DOI: 10.1371/journal.pone.0281385] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 01/22/2023] [Indexed: 07/01/2023] Open
Abstract
The gut microbiota-brain axis is a complex communication network essential for host health. Any long-term disruption can affect higher cognitive functions, or it may even result in several chronic neurological diseases. The type and diversity of nutrients an individual consumes are essential for developing the gut microbiota (GM) and the brain. Hence, dietary patterns might influence networks communication of this axis, especially at the age that both systems go through maturation processes. By implementing Mutual Information and Minimum Spanning Tree (MST); we proposed a novel combination of Machine Learning and Network Theory techniques to study the effect of animal protein and lipid intake on the connectivity of GM and brain cortex activity (BCA) networks in children from 5-to 10 years old from an indigenous community in the southwest of México. Socio-ecological conditions in this nonwestern lifestyle community are very homogeneous among its inhabitants but it shows high individual heterogeneity in the consumption of animal products. Results suggest that MST, the critical backbone of information flow, diminishes under low protein and lipid intake. So, under these nonwestern regimens, deficient animal protein and lipid consumption diets may significantly affect the GM-BCA connectivity in crucial development stages. Finally, MST offers us a metric that unifies biological systems of different nature to evaluate the change in their complexity in the face of environmental pressures or disturbances. Effect of Diet on gut microbiota and brain networks connectivity.
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Affiliation(s)
- Elvia Ramírez-Carrillo
- NeuroEcology Lab, Department of Psychology, UNAM, CDMX, México
- Investigadoras por México, Posdoc-CONACyT, Facultad de Psicología, Universidad Nacional Autónoma de México (UNAM), CDMX, México
| | - Isaac G-Santoyo
- NeuroEcology Lab, Department of Psychology, UNAM, CDMX, México
- Unidad de Investigación en Psicobiología y Neurociencias, Department of Psychology, Universidad Nacional Autónoma de México (UNAM), CDMX, México
| | - Oliver López-Corona
- Cátedras CONACyT, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México (UNAM), CDMX, México
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, CDMX, México
| | - Olga A. Rojas-Ramos
- NeuroEcology Lab, Department of Psychology, UNAM, CDMX, México
- Coordinación de Psciobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México (UNAM), CDMX, México
| | - Luisa I. Falcón
- Laboratorio de Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México, UNAM, Parque Científico y Tecnológico de Yucatán, Mérida, México
| | - Osiris Gaona
- Laboratorio de Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México, UNAM, Parque Científico y Tecnológico de Yucatán, Mérida, México
| | | | | | - Daniel Cerqueda-García
- Consorcio de Investigación del Golfo de México (CIGoM), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida, Departamento de Recursos del Mar, Mérida, Yucatán, México
| | - Andrés Sánchez-Quinto
- Laboratorio de Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México, UNAM, Parque Científico y Tecnológico de Yucatán, Mérida, México
| | - Diego Hernández-Muciño
- Laboratorio de Agroecología Instituto de Investigaciones en Ecosistema y Sustentabilidad, UNAM, Morelia, México
| | - Javier Nieto
- Laboratorio de Aprendizaje y Adaptación, Facultad de Psicología, Universidad Nacional Autónoma de México (UNAM), CDMX, México
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Ryu H, Habeck C, Stern Y, Lee S. Persistent homology-based functional connectivity and its association with cognitive ability: Life-span study. Hum Brain Mapp 2023; 44:3669-3683. [PMID: 37067099 PMCID: PMC10203816 DOI: 10.1002/hbm.26304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/10/2023] [Accepted: 03/25/2023] [Indexed: 04/18/2023] Open
Abstract
Brain-segregation attributes in resting-state functional networks have been widely investigated to understand cognition and cognitive aging using various approaches [e.g., average connectivity within/between networks and brain system segregation (BSS)]. While these approaches have assumed that resting-state functional networks operate in a modular structure, a complementary perspective assumes that a core-periphery or rich club structure accounts for brain functions where the hubs are tightly interconnected to each other to allow for integrated processing. In this article, we apply a novel method, persistent homology (PH), to develop an alternative to standard functional connectivity by quantifying the pattern of information during the integrated processing. We also investigate whether PH-based functional connectivity explains cognitive performance and compare the amount of variability in explaining cognitive performance for three sets of independent variables: (1) PH-based functional connectivity, (2) graph theory-based measures, and (3) BSS. Resting-state functional connectivity data were extracted from 279 healthy participants, and cognitive ability scores were generated in four domains (fluid reasoning, episodic memory, vocabulary, and processing speed). The results first highlight the pattern of brain-information flow over whole brain regions (i.e., integrated processing) accounts for more variance of cognitive abilities than other methods. The results also show that fluid reasoning and vocabulary performance significantly decrease as the strength of the additional information flow on functional connectivity with the shortest path increases. While PH has been applied to functional connectivity analysis in recent studies, our results demonstrate potential utility of PH-based functional connectivity in understanding cognitive function.
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Affiliation(s)
- Hyunnam Ryu
- Cognitive Neuroscience Division of the Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- Mental Health Data ScienceNew York State Psychiatric InstituteNew YorkNew YorkUSA
| | - Christian Habeck
- Cognitive Neuroscience Division of the Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Yaakov Stern
- Cognitive Neuroscience Division of the Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Seonjoo Lee
- Mental Health Data ScienceNew York State Psychiatric InstituteNew YorkNew YorkUSA
- Department of Biostatistics, Mailman School of Public HealthColumbia UniversityNew YorkNew YorkUSA
- Department of PsychiatryColumbia UniversityNew YorkNew YorkUSA
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Corona L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Stufflebeam SM, Pearl PL, Papadelis C. Non-invasive mapping of epileptogenic networks predicts surgical outcome. Brain 2023; 146:1916-1931. [PMID: 36789500 PMCID: PMC10151194 DOI: 10.1093/brain/awac477] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/03/2022] [Accepted: 11/30/2022] [Indexed: 02/16/2023] Open
Abstract
Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.
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Affiliation(s)
- Ludovica Corona
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Steve M Stufflebeam
- Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
- School of Medicine, Texas Christian University, Fort Worth, TX 76129, USA
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Chu M, Jiang D, Liu L, Nie B, Rosa-Neto P, Chen K, Wu L. Clinical relevance of disrupted topological organization of anatomical connectivity in behavioral variant frontotemporal dementia. Neurobiol Aging 2023; 124:29-38. [PMID: 36724600 PMCID: PMC11102657 DOI: 10.1016/j.neurobiolaging.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/13/2023]
Abstract
Graph theory is a novel approach used to examine the balance of brain connectomes. However, the clinical relevance of white matter (WM) connectome changes in the behavioral variant frontotemporal dementia (bvFTD) is not well understood. We aimed to investigate the clinical relevance of WM topological alterations in bvFTD. Thirty patients with probable bvFTD and 30 healthy controls underwent diffusion tensor imaging, structural MRI, and neuropsychological assessment. WM connectivity between 90 brain regions was calculated and the graph approach was applied to capture the individual characteristics of the anatomical network. Voxel-based morphometry and tract-based spatial statistics were used to present the gray matter atrophy and disrupted WM integrity. The topological organization was disrupted in patients with bvFTD both globally and locally. Compared to controls, bvFTD data showed a different pattern of hub region distributions. Notably, the nodal efficiency of the right superior orbital frontal gyrus was associated with apathy and disinhibition. Topological measures may be potential image markers for early diagnosis and disease severity monitoring of bvFTD.
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Affiliation(s)
- Min Chu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Deming Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Li Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Pedro Rosa-Neto
- McGill Centre for Studies in Aging, Alzheimer's Disease Research Unit, Montreal, Canada
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; College of Medicine-Phoenix, University of Arizona, Tucson, AZ, USA; School of Mathematics and Statistics, Arizona State University, Tempe, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Liyong Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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Gonzalez-Gomez R, Ibañez A, Moguilner S. Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference. Netw Neurosci 2023; 7:322-350. [PMID: 37333999 PMCID: PMC10270711 DOI: 10.1162/netn_a_00285] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/03/2022] [Indexed: 04/03/2024] Open
Abstract
Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.
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Affiliation(s)
- Raul Gonzalez-Gomez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Agustín Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Trinity College Dublin, Dublin, Ireland
| | - Sebastian Moguilner
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Ferreira LK, Lindberg O, Santillo AF, Wahlund LO. Functional connectivity in behavioral variant frontotemporal dementia. Brain Behav 2022; 12:e2790. [PMID: 36306386 PMCID: PMC9759144 DOI: 10.1002/brb3.2790] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/13/2022] [Accepted: 09/24/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Functional connectivity (FC)-which reflects relationships between neural activity in different brain regions-has been used to explore the functional architecture of the brain in neurodegenerative disorders. Although an increasing number of studies have explored FC changes in behavioral variant frontotemporal dementia (bvFTD), there is no focused, in-depth review about FC in bvFTD. METHODS Comprehensive literature search and narrative review to summarize the current field of FC in bvFTD. RESULTS (1) Decreased FC within the salience network (SN) is the most consistent finding in bvFTD; (2) FC changes extend beyond the SN and affect the interplay between networks; (3) results within the Default Mode Network are mixed; (4) the brain as a network is less interconnected and less efficient in bvFTD; (5) symptoms, functional impairment, and cognition are associated with FC; and (6) the functional architecture resembles patterns of neuropathological spread. CONCLUSIONS FC has potential as a biomarker, and future studies are expected to advance the field with multicentric initiatives, longitudinal designs, and methodological advances.
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Affiliation(s)
- Luiz Kobuti Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm, Sweden
| | - Olof Lindberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Alexander F Santillo
- Clinical Memory Research Unit and Psychiatry, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Shahdadian S, Wang X, Wanniarachchi H, Chaudhari A, Truong NCD, Liu H. Neuromodulation of brain power topography and network topology by prefrontal transcranial photobiomodulation. J Neural Eng 2022; 19:10.1088/1741-2552/ac9ede. [PMID: 36317341 PMCID: PMC9795815 DOI: 10.1088/1741-2552/ac9ede] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022]
Abstract
Objective.Transcranial photobiomodulation (tPBM) has shown promising benefits, including cognitive improvement, in healthy humans and in patients with Alzheimer's disease. In this study, we aimed to identify key cortical regions that present significant changes caused by tPBM in the electroencephalogram (EEG) oscillation powers and functional connectivity in the healthy human brain.Approach. A 64-channel EEG was recorded from 45 healthy participants during a 13 min period consisting of a 2 min baseline, 8 min tPBM/sham intervention, and 3 min recovery. After pre-processing and normalizing the EEG data at the five EEG rhythms, cluster-based permutation tests were performed for multiple comparisons of spectral power topographies, followed by graph-theory analysis as a topological approach for quantification of brain connectivity metrics at global and nodal/cluster levels.Main results. EEG power enhancement was observed in clusters of channels over the frontoparietal regions in the alpha band and the centroparietal regions in the beta band. The global measures of the network revealed a reduction in synchronization, global efficiency, and small-worldness of beta band connectivity, implying an enhancement of brain network complexity. In addition, in the beta band, nodal graphical analysis demonstrated significant increases in local information integration and centrality over the frontal clusters, accompanied by a decrease in segregation over the bilateral frontal, left parietal, and left occipital regions.Significance.Frontal tPBM increased EEG alpha and beta powers in the frontal-central-parietal regions, enhanced the complexity of the global beta-wave brain network, and augmented local information flow and integration of beta oscillations across prefrontal cortical regions. This study sheds light on the potential link between electrophysiological effects and human cognitive improvement induced by tPBM.
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Affiliation(s)
| | | | | | | | | | - Hanli Liu
- Authors to whom any correspondence should be addressed,
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Nigro S, Filardi M, Tafuri B, De Blasi R, Cedola A, Gigli G, Logroscino G. The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia. Front Neurol 2022; 13:910054. [PMID: 35837233 PMCID: PMC9275562 DOI: 10.3389/fneur.2022.910054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.
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Affiliation(s)
- Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Salvatore Nigro
| | - Marco Filardi
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Roberto De Blasi
- Department of Radiology, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy
| | - Alessia Cedola
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- *Correspondence: Giancarlo Logroscino
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Leng M, Sun Y, Chang H, Wang Z. Clustering Analysis of the Care Problems of People with Dementia Based on the Minimum Spanning Tree Algorithm: A Cross-Sectional Study. J Alzheimers Dis 2022; 87:1637-1646. [DOI: 10.3233/jad-215682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Recognizing the correlations between care problems of people with dementia could help clinicians choose treatment methods because related symptom groups might respond to the same treatment intervention. Objective: This study aimed to evaluate the prevalence of various care problems in people with dementia and to explore the core care problems and correlations between care problems of people with dementia. Methods: This cross-sectional study recruited family caregivers of people with dementia through memory clinics and WeChat groups. Care problems of people with dementia were measured using a care problems evaluation sheet, which involved three aspects: daily living care problems, behavioral and psychological symptoms, and safety risks. Clustering analysis of the care problems based on Kruskal’s minimum spanning tree (MST) algorithm was performed in the Jupyter Notebook software to explore the core care problems and their correlations. Results: A total of 687 carer-patient pairs were included in the analysis. In general, the prevalence of having difficulty in language performance, agitated behavior, and incidence of falls was relatively higher than other care problems in people with dementia, which distressed their family caregivers. Through clustering analysis, the 63 care problems were clustered into 7 clusters and 7 core care problems were identified. Conclusion: The prevalence of various care problems of people living with dementia in China was relatively high. The information regarding correlations in clusters among care problems will help practitioners and policymakers to identify the core care problems and optimize more rational treatments for people with dementia.
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Affiliation(s)
- Minmin Leng
- School of Nursing, Peking University, Beijing, China
- Peking University Health Science Centre for Evidence-Based Nursing: A Joanna Briggs Institute Affiliated Group, Beijing, China
| | - Yue Sun
- School of Nursing, Peking University, Beijing, China
- Peking University Health Science Centre for Evidence-Based Nursing: A Joanna Briggs Institute Affiliated Group, Beijing, China
| | - Hui Chang
- School of Nursing, Guizhou Medical University, Guizhou, China
| | - Zhiwen Wang
- School of Nursing, Peking University, Beijing, China
- Peking University Health Science Centre for Evidence-Based Nursing: A Joanna Briggs Institute Affiliated Group, Beijing, China
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11
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Blomsma N, de Rooy B, Gerritse F, van der Spek R, Tewarie P, Hillebrand A, Otte WM, Stam CJ, van Dellen E. Minimum spanning tree analysis of brain networks: A systematic review
of network size effects, sensitivity for neuropsychiatric pathology and disorder
specificity. Netw Neurosci 2022; 6:301-319. [PMID: 35733422 PMCID: PMC9207994 DOI: 10.1162/netn_a_00245] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/10/2022] [Indexed: 11/05/2022] Open
Abstract
Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments. The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.
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Affiliation(s)
- Nicky Blomsma
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Bart de Rooy
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Frank Gerritse
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Rick van der Spek
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Wim M. Otte
- University Medical Center Utrecht, Department of Child Neurology, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Cornelis Jan Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Edwin van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
- University Medical Center Utrecht, Department of Intensive Care Medicine, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
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12
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Franciotti R, Moretti DV, Benussi A, Ferri L, Russo M, Carrarini C, Barbone F, Arnaldi D, Falasca NW, Koch G, Cagnin A, Nobili FM, Babiloni C, Borroni B, Padovani A, Onofrj M, Bonanni L. Cortical network modularity changes along the course of frontotemporal and Alzheimer's dementing diseases. Neurobiol Aging 2021; 110:37-46. [PMID: 34847523 DOI: 10.1016/j.neurobiolaging.2021.10.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/07/2021] [Accepted: 10/26/2021] [Indexed: 12/14/2022]
Abstract
Cortical network modularity underpins cognitive functions, so we hypothesized its progressive derangement along the course of frontotemporal (FTD) and Alzheimer's (AD) dementing diseases. EEG was recorded in 18 FTD, 18 AD, and 20 healthy controls (HC). In the FTD and AD patients, the EEG recordings were performed at the prodromal stage of dementia, at the onset of dementia, and three years after the onset of dementia. HC underwent three EEG recordings at 2-3-year time interval. Information flows underlying EEG activity recorded at electrode pairs were estimated by means of Mutual Information (MI) analysis. The functional organization of the cortical network was modelled by means of the Graph theory analysis on MI adjacency matrices. Graph theory analysis showed that the main hub of HC (Parietal area) was lost in FTD patients at onset of dementia, substituted by provincial hubs in frontal leads. No changes in global network organization were found in AD. Despite a progressive cognitive impairment during the FTD and AD progression, only the FTD patients showed a derangement in the cortical network modularity, possibly due to dysfunctions in frontal functional connectivity.
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Affiliation(s)
- Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Davide V Moretti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Laura Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudia Carrarini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Filomena Barbone
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola W Falasca
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | | | - Flavio M Nobili
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer," Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino (FR), Cassino, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.
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13
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Yin Z, Wang Y, Dong M, Ren S, Hu H, Yin K, Liang J. Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study. Front Neurosci 2021; 15:652920. [PMID: 34177446 PMCID: PMC8221185 DOI: 10.3389/fnins.2021.652920] [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] [Received: 01/13/2021] [Accepted: 05/17/2021] [Indexed: 12/03/2022] Open
Abstract
Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0-100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100-200 and 200-300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.
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Affiliation(s)
- Zhongliang Yin
- School of Electronic Engineering, Xidian University, Xi'an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Yue Wang
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Shenghan Ren
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Haihong Hu
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Kuiying Yin
- Nanjing Research Institute of Electronics Technology, Nanjing, China
| | - Jimin Liang
- School of Electronic Engineering, Xidian University, Xi'an, China
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14
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Liu X, Yang H, Becker B, Huang X, Luo C, Meng C, Biswal B. Disentangling age- and disease-related alterations in schizophrenia brain network using structural equation modeling: A graph theoretical study based on minimum spanning tree. Hum Brain Mapp 2021; 42:3023-3041. [PMID: 33960579 PMCID: PMC8193510 DOI: 10.1002/hbm.25403] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 02/05/2023] Open
Abstract
Functional brain networks have been shown to undergo fundamental changes associated with aging or schizophrenia. However, the mechanism of how these factors exert influences jointly or interactively on brain networks remains elusive. A unified recognition of connectomic alteration patterns was also hampered by heterogeneities in network construction and thresholding methods. Recently, an unbiased network representation method regardless of network thresholding, so called minimal spanning tree algorithm, has been applied to study the critical skeleton of the brain network. In this study, we aimed to use minimum spanning tree (MST) as an unbiased network reconstruction and employed structural equation modeling (SEM) to unravel intertwined relationships among multiple phenotypic and connectomic variables in schizophrenia. First, we examined global and local brain network properties in 40 healthy subjects and 40 schizophrenic patients aged 21–55 using resting‐state functional magnetic resonance imaging (rs‐fMRI). Global network alterations are measured by graph theoretical metrics of MSTs and a connectivity‐transitivity two‐dimensional approach was proposed to characterize nodal roles. We found that networks of schizophrenic patients exhibited a more star‐like global structure compared to controls, indicating excessive integration, and a loss of regional transitivity in the dorsal frontal cortex (corrected p <.05). Regional analysis of MST network topology revealed that schizophrenia patients had more network hubs in frontal regions, which may be linked to the “overloading” hypothesis. Furthermore, using SEM, we found that the level of MST integration mediated the influence of age on negative symptom severity (indirect effect 95% CI [0.026, 0.449]). These findings highlighted an altered network skeleton in schizophrenia and suggested that aging‐related enhancement of network integration may undermine functional specialization of distinct neural systems and result in aggravated schizophrenic symptoms.
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Affiliation(s)
- Xinyu Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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15
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Cui L, Chen K, Huang L, Sun J, Lv Y, Jia X, Guo Q. Changes in local brain function in mild cognitive impairment due to semantic dementia. CNS Neurosci Ther 2021; 27:587-602. [PMID: 33650764 PMCID: PMC8025655 DOI: 10.1111/cns.13621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/24/2021] [Accepted: 01/28/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS Mild cognitive impairment due to semantic dementia represents the preclinical stage, involving cognitive decline dominated by semantic impairment below the semantic dementia standard. Therefore, studying mild cognitive impairment due to semantic dementia may identify changes in patients before progression to dementia. However, whether changes in local functional activity occur in preclinical stages of semantic dementia remains unknown. Here, we explored local functional changes in patients with mild cognitive impairment due to semantic dementia using resting-state functional MRI. METHODS We administered a battery of neuropsychological tests to twenty-two patients with mild cognitive impairment due to semantic dementia (MCI-SD group) and nineteen healthy controls (HC group). We performed structural MRI to compare gray matter volumes, and resting-state functional MRI with multiple sub-bands and indicators to evaluate functional activity. RESULTS Neuropsychological tests revealed a significant decline in semantic performance in the MCI-SD group, but no decline in other cognitive domains. Resting-state functional MRI revealed local functional changes in multiple brain regions in the MCI-SD group, distributed in different sub-bands and indicators. In the normal band, local functional changes were only in the gray matter atrophic area. In the other sub-bands, more regions with local functional changes outside atrophic areas were found across various indicators. Among these, the degree centrality of the left precuneus in the MCI-SD group was positively correlated with general semantic tasks (oral sound naming, word-picture verification). CONCLUSION Our study revealed local functional changes in mild cognitive impairment due to semantic dementia, some of which were located outside the atrophic gray matter. Driven by functional connectivity changes, the left precuneus might play a role in preclinical semantic dementia. The study proved the value of frequency-dependent sub-bands, especially the slow-2 and slow-3 sub-bands.
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Affiliation(s)
- Liang Cui
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Keliang Chen
- Department of NeurologyHuashan HospitalFudan UniversityShanghaiChina
| | - Lin Huang
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Jiawei Sun
- School of Information and Electronics TechnologyJiamusi UniversityJiamusiChina
| | - Yating Lv
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Xize Jia
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Qihao Guo
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
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16
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Nigro S, Tafuri B, Urso D, De Blasi R, Frisullo ME, Barulli MR, Capozzo R, Cedola A, Gigli G, Logroscino G. Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia. Brain Sci 2021; 11:brainsci11020192. [PMID: 33557411 PMCID: PMC7915789 DOI: 10.3390/brainsci11020192] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 11/17/2022] Open
Abstract
Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy controls. In particular, 25 bvFTD patients and 20 healthy controls underwent structural 3T MRI. Next, bilaterally averaged values of 34 cortical surface areas, 34 cortical thickness values, and six subcortical volumes were used to capture single-subject anatomical connectivity and investigate network organization using a graph theory approach. Relative to controls, bvFTD patients showed altered small-world properties and decreased global efficiency, suggesting a reduced ability to combine specialized information from distributed brain regions. At a local level, patients with bvFTD displayed lower values of local efficiency in the cortical thickness of the caudal and rostral middle frontal gyrus, rostral anterior cingulate, and precuneus, cuneus, and transverse temporal gyrus. A significant correlation was also found between the efficiency of caudal anterior cingulate thickness and Mini-Mental State Examination (MMSE) scores in bvFTD patients. Taken together, these findings confirm the selective disruption in structural brain networks of bvFTD patients, providing new insights on the association between cognitive decline and graph properties.
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Affiliation(s)
- Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, 73100 Lecce, Italy; (S.N.); (A.C.); (G.G.)
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Daniele Urso
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
- Department of Neurosciences, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London SE5 8AF, UK
| | - Roberto De Blasi
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
- Department of Radiology, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy
| | - Maria Elisa Frisullo
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Maria Rosaria Barulli
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Rosa Capozzo
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Alessia Cedola
- Institute of Nanotechnology (NANOTEC), National Research Council, 73100 Lecce, Italy; (S.N.); (A.C.); (G.G.)
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council, 73100 Lecce, Italy; (S.N.); (A.C.); (G.G.)
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Campus Ecotekne, 73100 Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari ‘Aldo Moro’, 70124 Bari, Italy
- Correspondence: or giancarlo.; Tel.: +39-0833/773904
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17
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Dev SI, Dickerson BC, Touroutoglou A. Neuroimaging in Frontotemporal Lobar Degeneration: Research and Clinical Utility. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1281:93-112. [PMID: 33433871 PMCID: PMC8787866 DOI: 10.1007/978-3-030-51140-1_7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
Frontotemporal lobar dementia (FTLD) is a clinically and pathologically complex disease. Advances in neuroimaging techniques have provided a specialized set of tools to investigate underlying pathophysiology and identify clinical biomarkers that aid in diagnosis, prognostication, monitoring, and identification of appropriate endpoints in clinical trials. In this chapter, we review data discussing the utility of neuroimaging biomarkers in sporadic FTLD, with an emphasis on current and future clinical applications. Among those modalities readily utilized in clinical settings, T1-weighted structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are best supported in differential diagnosis and as targets for clinical trial endpoints. However, a number of nonclinical neuroimaging modalities, including diffusion tensor imaging and resting-state functional connectivity MRI, show promise as biomarkers to predict progression and as clinical trial endpoints. Other neuroimaging modalities, including amyloid PET, Tau PET, and arterial spin labeling MRI, are also discussed, though more work is required to establish their utility in FTLD in clinical settings.
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Affiliation(s)
- Sheena I Dev
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA.
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
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18
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Simos NJ, Dimitriadis SI, Kavroulakis E, Manikis GC, Bertsias G, Simos P, Maris TG, Papadaki E. Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach. Brain Sci 2020; 10:brainsci10110777. [PMID: 33113768 PMCID: PMC7692139 DOI: 10.3390/brainsci10110777] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 01/15/2023] Open
Abstract
Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.
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Affiliation(s)
- Nicholas John Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
| | - Stavros I. Dimitriadis
- Integrative Neuroimaging Lab, 55133 Thessaloniki, Greece;
- 1st Department of Neurology, G.H. “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Research Institute School of Medicine, & MRC Centre for Neuropsychiatric Genetics and Genomics, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
| | - Eleftherios Kavroulakis
- Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
| | - Georgios C. Manikis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
| | - George Bertsias
- Department of Rheumatology, Clinical Immunology and Allergy, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
| | - Panagiotis Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Psychiatry, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece
- Correspondence: or
| | - Thomas G. Maris
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
| | - Efrosini Papadaki
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
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Bonanni L, Moretti D, Benussi A, Ferri L, Russo M, Carrarini C, Barbone F, Arnaldi D, Falasca NW, Koch G, Cagnin A, Nobili F, Babiloni C, Borroni B, Padovani A, Onofrj M, Franciotti R. Hyperconnectivity in Dementia Is Early and Focal and Wanes with Progression. Cereb Cortex 2020; 31:97-105. [DOI: 10.1093/cercor/bhaa209] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/19/2020] [Accepted: 07/08/2020] [Indexed: 12/14/2022] Open
Abstract
Abstract
We investigated in a longitudinal multicenter cohort study functional cortical connectivity changes along the course of frontotemporal dementia (FTD) and Alzheimer’s disease (AD) from the prodromal stage of the diseases.
Electroencephalography (EEG) was recorded in 18 FTD and 18 AD patients at the prodromal stage of dementia, at dementia onset, and 3 years after dementia onset. Twenty healthy controls (HC) underwent EEG recordings at the same time interval as the patients.
Mutual information (MI) analysis measured the strength of functional network connectivity.
FTD and AD patients showed greater MI at the prodromal stage of dementia (FTD vs. HC P = 2 × 10−8; AD vs. HC P = 4 × 10–3). Local connectivity was higher in left and right frontal areas of FTD (P = 7 × 10−5 and 0.03) and in left and right posterior areas in AD (P = 3 × 10−5 and 5 × 10−5) versus HC.
We showed cortical hyperconnectivity at the prodromal stage of dementia in areas involved in the specific pathological process of FTD (frontal regions) and AD (posterior regions). Hyperconnectivity disappeared during follow-up, thus suggesting that it is an early electrophysiological feature of dementia, potentially useful to identify prodromal FTD and AD.
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Affiliation(s)
- Laura Bonanni
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Davide Moretti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Alzheimer Unit, 25125 Brescia, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, 25040 Brescia, Italy
| | - Laura Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Claudia Carrarini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Filomena Barbone
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, 16132 Genoa, Italy
- U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Nicola Walter Falasca
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, 00179 Rome, Italy
| | - Annachiara Cagnin
- Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, 00184 Rome, Italy
- Department of Neurosciences, University of Padua, 35128 Padova, Italy
| | - Flavio Nobili
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, 16132 Genoa, Italy
- U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, 00185 Rome, Italy
- Hospital San Raffaele Cassino (FR), 03043 Cassino, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, 25040 Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, 25040 Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
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Agadagba SK, Chan LLH. Spontaneous Feedforward Connectivity in Electrically Stimulated Retinal Degeneration Mice . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3513-3516. [PMID: 33018761 DOI: 10.1109/embc44109.2020.9175231] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Retinal degeneration (Rd) is a neurodegenerative disorder primarily associated with the degeneration of the retina neurons and culminates in the eventual loss of visual perception or blindness. Decrease in fronto-, parietal and occipital brain connectivity have been reported in a number of neurodegeneration diseases involving cognitive decline. However, cortical communication in the brain of retinal degeneration patients remains largely unknown and strategies to remediate observed dysfunctional brain connectivity in such instance have not be thoroughly investigated. We used rd10 mice as a model to study brain connectivity in the human retinal degeneration disease, retinitis pigmentosa. Rd10 mice with sham matched controls were electrically stimulated at varying stimulation frequencies and the consequent perturbations in feedforward brain connectivity were studied in the visual cortex and pre-frontal cortex using electrocorticography (ECoG) and normalized symbolic transfer entropy (NSTE). Contra Vcx - contra PFx feed forward connectivity significantly (p<0.05) increased in theta, alpha and beta oscillatory bands of 2 Hz and 10 Hz stimulated rd10 respectively in comparison with sham group. Also, this increase was significantly maintained even after the end of the stimulation period.
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21
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Functional Connectivity in Neurodegenerative Disorders: Alzheimer's Disease and Frontotemporal Dementia. Top Magn Reson Imaging 2020; 28:317-324. [PMID: 31794504 DOI: 10.1097/rmr.0000000000000223] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Neurodegenerative disorders are a growing cause of morbidity and mortality worldwide. Onset is typically insidious and clinical symptoms of behavioral change, memory loss, or cognitive dysfunction may not be evident early in the disease process. Efforts have been made to discover biomarkers that allow for earlier diagnosis of neurodegenerative disorders, to initiate treatment that may slow the course of clinical deterioration. Neuronal dysfunction occurs earlier than clinical symptoms manifest. Thus, assessment of neuronal function using functional brain imaging has been examined as a potential biomarker. While most early studies used task-functional magnetic resonance imaging (fMRI), with the more recent technique of resting-state fMRI, "intrinsic" relationships between brain regions or brain networks have been studied in greater detail in neurodegenerative disorders. In Alzheimer's disease, the most common neurodegenerative disorder, and frontotemporal dementia, another of the common dementias, specific brain networks may be particularly susceptible to dysfunction. In this review, we highlight the major findings of functional connectivity assessed by resting state fMRI in Alzheimer's disease and frontotemporal dementia.
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