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Odimayo S, Olisah CC, Mohammed K. Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease. Sci Rep 2024; 14:15270. [PMID: 38961114 PMCID: PMC11222499 DOI: 10.1038/s41598-024-60611-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/25/2024] [Indexed: 07/05/2024] Open
Abstract
Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.
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Affiliation(s)
- Simisola Odimayo
- School of Engineering, University of the West of England, Bristol, UK
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Yang C, Liu G, Chen X, Le W. Cerebellum in Alzheimer's disease and other neurodegenerative diseases: an emerging research frontier. MedComm (Beijing) 2024; 5:e638. [PMID: 39006764 PMCID: PMC11245631 DOI: 10.1002/mco2.638] [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: 10/30/2023] [Revised: 06/04/2024] [Accepted: 06/12/2024] [Indexed: 07/16/2024] Open
Abstract
The cerebellum is crucial for both motor and nonmotor functions. Alzheimer's disease (AD), alongside other dementias such as vascular dementia (VaD), Lewy body dementia (DLB), and frontotemporal dementia (FTD), as well as other neurodegenerative diseases (NDs) like Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and spinocerebellar ataxias (SCA), are characterized by specific and non-specific neurodegenerations in central nervous system. Previously, the cerebellum's significance in these conditions was underestimated. However, advancing research has elevated its profile as a critical node in disease pathology. We comprehensively review the existing evidence to elucidate the relationship between cerebellum and the aforementioned diseases. Our findings reveal a growing body of research unequivocally establishing a link between the cerebellum and AD, other forms of dementia, and other NDs, supported by clinical evidence, pathological and biochemical profiles, structural and functional neuroimaging data, and electrophysiological findings. By contrasting cerebellar observations with those from the cerebral cortex and hippocampus, we highlight the cerebellum's distinct role in the disease processes. Furthermore, we also explore the emerging therapeutic potential of targeting cerebellum for the treatment of these diseases. This review underscores the importance of the cerebellum in these diseases, offering new insights into the disease mechanisms and novel therapeutic strategies.
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Affiliation(s)
- Cui Yang
- Institute of Neurology Sichuan Provincial People's Hospital School of Medicine University of Electronic Science and Technology of China Chengdu China
| | - Guangdong Liu
- Institute of Neurology Sichuan Provincial People's Hospital School of Medicine University of Electronic Science and Technology of China Chengdu China
| | - Xi Chen
- Institute of Neurology Sichuan Provincial People's Hospital School of Medicine University of Electronic Science and Technology of China Chengdu China
| | - Weidong Le
- Institute of Neurology Sichuan Provincial People's Hospital School of Medicine University of Electronic Science and Technology of China Chengdu China
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Khalilullah KMI, Agcaoglu O, Sui J, Duda M, Adali T, Calhoun VD. Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586091. [PMID: 38585901 PMCID: PMC10996497 DOI: 10.1101/2024.03.21.586091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.
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Affiliation(s)
- K M Ibrahim Khalilullah
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Oktay Agcaoglu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Marlena Duda
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Tülay Adali
- Department of Electrical and Computer Engineering, University of Maryland, Baltimore, Maryland, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
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Seoane S, van den Heuvel M, Acebes Á, Janssen N. The subcortical default mode network and Alzheimer's disease: a systematic review and meta-analysis. Brain Commun 2024; 6:fcae128. [PMID: 38665961 PMCID: PMC11043657 DOI: 10.1093/braincomms/fcae128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/28/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
The default mode network is a central cortical brain network suggested to play a major role in several disorders and to be particularly vulnerable to the neuropathological hallmarks of Alzheimer's disease. Subcortical involvement in the default mode network and its alteration in Alzheimer's disease remains largely unknown. We performed a systematic review, meta-analysis and empirical validation of the subcortical default mode network in healthy adults, combined with a systematic review, meta-analysis and network analysis of the involvement of subcortical default mode areas in Alzheimer's disease. Our results show that, besides the well-known cortical default mode network brain regions, the default mode network consistently includes subcortical regions, namely the thalamus, lobule and vermis IX and right Crus I/II of the cerebellum and the amygdala. Network analysis also suggests the involvement of the caudate nucleus. In Alzheimer's disease, we observed a left-lateralized cluster of decrease in functional connectivity which covered the medial temporal lobe and amygdala and showed overlap with the default mode network in a portion covering parts of the left anterior hippocampus and left amygdala. We also found an increase in functional connectivity in the right anterior insula. These results confirm the consistency of subcortical contributions to the default mode network in healthy adults and highlight the relevance of the subcortical default mode network alteration in Alzheimer's disease.
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Affiliation(s)
- Sara Seoane
- Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
- Institute of Biomedical Technologies (ITB), University of La Laguna, Tenerife 38200, Spain
- Instituto Universitario de Neurociencia (IUNE), University of La Laguna, Tenerife 38200, Spain
| | - Martijn van den Heuvel
- Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
- Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam 1081 HV, The Netherlands
| | - Ángel Acebes
- Institute of Biomedical Technologies (ITB), University of La Laguna, Tenerife 38200, Spain
- Department of Basic Medical Sciences, University of La Laguna, Tenerife 38200, Spain
| | - Niels Janssen
- Institute of Biomedical Technologies (ITB), University of La Laguna, Tenerife 38200, Spain
- Instituto Universitario de Neurociencia (IUNE), University of La Laguna, Tenerife 38200, Spain
- Department of Cognitive, Social and Organizational Psychology, University of La Laguna, Tenerife 38200, Spain
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Arleo A, Bareš M, Bernard JA, Bogoian HR, Bruchhage MMK, Bryant P, Carlson ES, Chan CCH, Chen LK, Chung CP, Dotson VM, Filip P, Guell X, Habas C, Jacobs HIL, Kakei S, Lee TMC, Leggio M, Misiura M, Mitoma H, Olivito G, Ramanoël S, Rezaee Z, Samstag CL, Schmahmann JD, Sekiyama K, Wong CHY, Yamashita M, Manto M. Consensus Paper: Cerebellum and Ageing. CEREBELLUM (LONDON, ENGLAND) 2024; 23:802-832. [PMID: 37428408 PMCID: PMC10776824 DOI: 10.1007/s12311-023-01577-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/11/2023]
Abstract
Given the key roles of the cerebellum in motor, cognitive, and affective operations and given the decline of brain functions with aging, cerebellar circuitry is attracting the attention of the scientific community. The cerebellum plays a key role in timing aspects of both motor and cognitive operations, including for complex tasks such as spatial navigation. Anatomically, the cerebellum is connected with the basal ganglia via disynaptic loops, and it receives inputs from nearly every region in the cerebral cortex. The current leading hypothesis is that the cerebellum builds internal models and facilitates automatic behaviors through multiple interactions with the cerebral cortex, basal ganglia and spinal cord. The cerebellum undergoes structural and functional changes with aging, being involved in mobility frailty and related cognitive impairment as observed in the physio-cognitive decline syndrome (PCDS) affecting older, functionally-preserved adults who show slowness and/or weakness. Reductions in cerebellar volume accompany aging and are at least correlated with cognitive decline. There is a strongly negative correlation between cerebellar volume and age in cross-sectional studies, often mirrored by a reduced performance in motor tasks. Still, predictive motor timing scores remain stable over various age groups despite marked cerebellar atrophy. The cerebello-frontal network could play a significant role in processing speed and impaired cerebellar function due to aging might be compensated by increasing frontal activity to optimize processing speed in the elderly. For cognitive operations, decreased functional connectivity of the default mode network (DMN) is correlated with lower performances. Neuroimaging studies highlight that the cerebellum might be involved in the cognitive decline occurring in Alzheimer's disease (AD), independently of contributions of the cerebral cortex. Grey matter volume loss in AD is distinct from that seen in normal aging, occurring initially in cerebellar posterior lobe regions, and is associated with neuronal, synaptic and beta-amyloid neuropathology. Regarding depression, structural imaging studies have identified a relationship between depressive symptoms and cerebellar gray matter volume. In particular, major depressive disorder (MDD) and higher depressive symptom burden are associated with smaller gray matter volumes in the total cerebellum as well as the posterior cerebellum, vermis, and posterior Crus I. From the genetic/epigenetic standpoint, prominent DNA methylation changes in the cerebellum with aging are both in the form of hypo- and hyper-methylation, and the presumably increased/decreased expression of certain genes might impact on motor coordination. Training influences motor skills and lifelong practice might contribute to structural maintenance of the cerebellum in old age, reducing loss of grey matter volume and therefore contributing to the maintenance of cerebellar reserve. Non-invasive cerebellar stimulation techniques are increasingly being applied to enhance cerebellar functions related to motor, cognitive, and affective operations. They might enhance cerebellar reserve in the elderly. In conclusion, macroscopic and microscopic changes occur in the cerebellum during the lifespan, with changes in structural and functional connectivity with both the cerebral cortex and basal ganglia. With the aging of the population and the impact of aging on quality of life, the panel of experts considers that there is a huge need to clarify how the effects of aging on the cerebellar circuitry modify specific motor, cognitive, and affective operations both in normal subjects and in brain disorders such as AD or MDD, with the goal of preventing symptoms or improving the motor, cognitive, and affective symptoms.
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Affiliation(s)
- Angelo Arleo
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Martin Bareš
- First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's Teaching Hospital, Brno, Czech Republic
- Department of Neurology, School of Medicine, University of Minnesota, Minneapolis, USA
| | - Jessica A Bernard
- Department of Psychological and Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843, USA
- Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX, USA
| | - Hannah R Bogoian
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Muriel M K Bruchhage
- Department of Psychology, Stavanger University, Institute of Social Sciences, Kjell Arholms Gate 41, 4021, Stavanger, Norway
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Centre for Neuroimaging Sciences, Box 89, De Crespigny Park, London, PO, SE5 8AF, UK
- Rhode Island Hospital, Department for Diagnostic Imaging, 1 Hoppin St, Providence, RI, 02903, USA
- Department of Paediatrics, Warren Alpert Medical School of Brown University, 222 Richmond St, Providence, RI, 02903, USA
| | - Patrick Bryant
- Freie Universität Berlin, Fachbereich Mathematik und Informatik, Arnimallee 12, 14195, Berlin, Germany
| | - Erik S Carlson
- Department of Psychiatry and Behavioural Sciences, University of Washington, Seattle, WA, USA
- Geriatric Research, Education and Clinical Center, Veteran's Affairs Medical Center, Puget Sound, Seattle, WA, USA
| | - Chetwyn C H Chan
- Department of Psychology, The Education University of Hong Kong, New Territories, Tai Po, Hong Kong, China
| | - Liang-Kung Chen
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan
- Center for Geriatric and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
- Taipei Municipal Gan-Dau Hospital (managed by Taipei Veterans General Hospital), Taipei, Taiwan
| | - Chih-Ping Chung
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Vonetta M Dotson
- Department of Psychology, Georgia State University, Atlanta, GA, USA
- Gerontology Institute, Georgia State University, Atlanta, GA, USA
| | - Pavel Filip
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Xavier Guell
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Laboratory for Neuroanatomy and Cerebellar Neurobiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Christophe Habas
- CHNO Des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, 75012, Paris, France
- Université Versailles St Quentin en Yvelines, Paris, France
| | - Heidi I L Jacobs
- School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, PO BOX 616, 6200, MD, Maastricht, The Netherlands
- Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, PO BOX 616, 6200, MD, Maastricht, The Netherlands
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Tatia M C Lee
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Laboratory of Neuropsychology and Human Neuroscience, Department of Psychology, The University of Hong Kong, Hong Kong, China
| | - Maria Leggio
- Department of Psychology, Sapienza University of Rome, Rome, Italy
- Ataxia Laboratory, I.R.C.C.S. Santa Lucia Foundation, Rome, Italy
| | - Maria Misiura
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Hiroshi Mitoma
- Department of Medical Education, Tokyo Medical University, Tokyo, Japan
| | - Giusy Olivito
- Department of Psychology, Sapienza University of Rome, Rome, Italy
- Ataxia Laboratory, I.R.C.C.S. Santa Lucia Foundation, Rome, Italy
| | - Stephen Ramanoël
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
- Université Côte d'Azur, LAMHESS, Nice, France
| | - Zeynab Rezaee
- Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, NIH, Bethesda, USA
| | - Colby L Samstag
- Department of Psychiatry and Behavioural Sciences, University of Washington, Seattle, WA, USA
- Geriatric Research, Education and Clinical Center, Veteran's Affairs Medical Center, Puget Sound, Seattle, WA, USA
| | - Jeremy D Schmahmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Laboratory for Neuroanatomy and Cerebellar Neurobiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Ataxia Center, Cognitive Behavioural neurology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kaoru Sekiyama
- Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, Kyoto, Japan
| | - Clive H Y Wong
- Department of Psychology, The Education University of Hong Kong, New Territories, Tai Po, Hong Kong, China
| | - Masatoshi Yamashita
- Research Center for Child Mental Development, University of Fukui, Fukui, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Osaka, Japan
| | - Mario Manto
- Service de Neurologie, Médiathèque Jean Jacquy, CHU-Charleroi, Charleroi, Belgium.
- Service des Neurosciences, University of Mons, Mons, Belgium.
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Zhang S, Ge M, Cheng H, Chen S, Li Y, Wang K. Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine. BMC Med Imaging 2024; 24:72. [PMID: 38532313 DOI: 10.1186/s12880-024-01250-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 03/15/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Quantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To identify biomarkers associated with cognitive performance in the older, this study combined an index model specific for resting-state functional connectivity (FC) with a supervised machine learning method. METHODS Performance scores on conventional cognitive test scores and resting-state functional MRI data were obtained for 98 healthy older individuals and 90 healthy youth from two public databases. Based on the test scores, the older cohort was categorized into two groups: excellent and poor. A resting-state FC scores model (rs-FCSM) was constructed for each older individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using this model. To suggest the effectiveness of constructed model, the scores of these brain areas were used as feature matrix inputs for training an extreme learning machine. classification accuracy (CA) was then tested in separate groups and validated by N-fold cross-validation. RESULTS This learning study could effectively classify the cognitive status of healthy older individuals according to the model scores of frontal lobe, temporal lobe, and parietal lobe with a mean accuracy of 86.67%, which is higher than that achieved using conventional correlation analysis. CONCLUSION This classification study of the rs-FCSM may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms.
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Affiliation(s)
- Shiying Zhang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China.
- Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China.
- Tianjin Hebei University of Technology, 5340 Xiping Road, Beichen District, Tianjin, 300130, China.
| | - Manling Ge
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China.
- Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China.
- Hebei University of Technology, 8 Guangrong Road, Hongqiao District, Tianjin, 300130, China.
| | - Hao Cheng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
- Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China
| | - Shenghua Chen
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
- Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Yihui Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
- Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Kaiwei Wang
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China
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Herrejon IA, Jackson TB, Hicks TH, Bernard JA. Functional Connectivity Differences in Distinct Dentato-Cortical Networks in Alzheimer's Disease and Mild Cognitive Impairment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578249. [PMID: 38352603 PMCID: PMC10862898 DOI: 10.1101/2024.02.02.578249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Recent research has implicated the cerebellum in Alzheimer's disease (AD), and cerebrocerebellar network connectivity is emerging as a possible contributor to symptom severity. The cerebellar dentate nucleus (DN) has parallel motor and non-motor sub-regions that project to motor and frontal regions of the cerebral cortex, respectively. These distinct dentato-cortical networks have been delineated in the non-human primate and human brain. Importantly, cerebellar regions prone to atrophy in AD are functionally connected to atrophied regions of the cerebral cortex, suggesting that dysfunction perhaps occurs at a network level. Investigating functional connectivity (FC) alterations of the DN is a crucial step in understanding the cerebellum in AD and in mild cognitive impairment (MCI). Inclusion of this latter group stands to provide insights into cerebellar contributions prior to diagnosis of AD. The present study investigated FC differences in dorsal (dDN) and ventral (vDN) DN networks in MCI and AD relative to cognitively normal participants (CN) and relationships between FC and behavior. Our results showed patterns indicating both higher and lower functional connectivity in both dDN and vDN in AD compared to CN. However, connectivity in the AD group was lower when compared to MCI. We argue that these findings suggest that the patterns of higher FC in AD may act as a compensatory mechanism. Additionally, we found associations between the individual networks and behavior. There were significant interactions between dDN connectivity and motor symptoms. However, both DN seeds were associated with cognitive task performance. Together, these results indicate that cerebellar DN networks are impacted in AD, and this may impact behavior. In concert with the growing body of literature implicating the cerebellum in AD, our work further underscores the importance of investigations of this region. We speculate that much like in psychiatric diseases such as schizophrenia, cerebellar dysfunction results in negative impacts on thought and the organization therein. Further, this is consistent with recent arguments that the cerebellum provides crucial scaffolding for cognitive function in aging. Together, our findings stand to inform future clinical work in the diagnosis and understanding of this disease.
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Affiliation(s)
- Ivan A. Herrejon
- Department of Psychological and Brain Sciences Texas A&M University
| | - T. Bryan Jackson
- Department of Psychological and Brain Sciences Texas A&M University
- Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center
| | - Tracey H. Hicks
- Department of Psychological and Brain Sciences Texas A&M University
| | - Jessica A. Bernard
- Department of Psychological and Brain Sciences Texas A&M University
- Texas A&M Institute for Neuroscience Texas A&M University
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8
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Kim HE, Kim JJ, Seok JH, Park JY, Oh J. Resting-state functional connectivity and cognitive performance in aging adults with cognitive decline: A data-driven multivariate pattern analysis. Compr Psychiatry 2024; 129:152445. [PMID: 38154288 DOI: 10.1016/j.comppsych.2023.152445] [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: 05/05/2023] [Revised: 11/23/2023] [Accepted: 12/14/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND Cognitive impairments occur on a continuous spectrum in multiple cognitive domains showing individual variability of the deteriorating patterns; however, often, cognitive domains are studied separately. METHODS The present study investigated aging individual variations of cognitive abilities and related resting-state functional connectivity (rsFC) using data-driven approach. Cognitive and neuroimaging data were obtained from 62 elderly outpatients with cognitive decline. Principal component analysis (PCA) was conducted on the cognitive data to determine patterns of cognitive performance, then data-driven whole-brain connectome multivariate pattern analysis (MVPA) was applied on the neuroimaging data to discover neural regions associated with the cognitive characteristic. RESULTS The first component (PC1) delineated an overall decline in all domains of cognition, and the second component (PC2) represented a compensatory relationship within basic cognitive functions. MVPA indicated rsFC of the cerebellum lobule VIII and insula with the default-mode network, frontoparietal network, and salience network inversely correlated with PC1 scores. Additionally, PC2 score was related to rsFC patterns with temporal pole and occipital cortex. CONCLUSIONS The featured primary cognitive characteristic depicted the importance of the cerebellum and insula connectivity patterns in of the general cognitive decline. The findings also discovered a secondary characteristic that communicated impaired interactions within the basic cognitive function, which was independent from the impairment severity.
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Affiliation(s)
- Hesun Erin Kim
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Jin Kim
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jeong-Ho Seok
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin Young Park
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea
| | - Jooyoung Oh
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Saha DK, Bohsali A, Saha R, Hajjar I, Calhoun VD. Neuromark PET: A multivariate method for Estimating and comparing whole brain functional networks and connectomes from fMRI and PET data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575131. [PMID: 38260682 PMCID: PMC10802620 DOI: 10.1101/2024.01.10.575131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Positron emission tomography (PET) and magnetic resonance imaging (MRI) are both widely used neuroimaging techniques to study brain function. Although whole brain resting functional MRI (fMRI) connectomes are widely used, the integration or association of whole brain functional connectomes with PET data are rarely done. This likely stems from the fact that PET data is typically analyzed by using a regions of interest approach, while whole brain spatial networks and their connectivity (covariation) receive much less attention. As a result, to date, there have been no direct comparisons between whole brain PET and fMRI connectomes. In this study, we present a method that uses spatially constrained independent component analysis (scICA) to estimate corresponding PET and fMRI connectomes and examine the relationship between them using mild cognitive impairment (MCI) datasets. Our results demonstrate highly modularized PET connectome patterns that complement those identified from resting fMRI. In particular, fMRI showed strong intra-domain connectivity with interdomain anticorrelation in sensorimotor and visual domains as well as default mode network. PET amyloid data showed similar strong intra-domain effects, but showed much higher correlations within cognitive control and default mode domains, as well as anticorrelation between cerebellum and other domains. The estimated PET networks have similar, but not identical, network spatial patterns to the resting fMRI networks, with the PET networks being slightly smoother and, in some cases, showing variations in subnodes. We also analyzed the differences between individuals with MCI receiving medication versus a placebo. Results show both common and modality specific treatment effects on fMRI and PET connectomes. From our fMRI analysis, we observed higher activation differences in various regions, such as the connection between the thalamus and middle occipital gyrus, as well as the insula and right middle occipital gyrus. Meanwhile, the PET analysis revealed increased activation between the anterior cingulate cortex and the left inferior parietal lobe, along with other regions, in individuals who received medication versus placebo. In sum, our novel approach identifies corresponding whole-brain PET and fMRI networks and connectomes. While we observed common patterns of network connectivity, our analysis of the MCI treatment and placebo groups revealed that each modality identifies a unique set of networks, highlighting differences between the two groups.
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Affiliation(s)
- Debbrata K. Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303
| | - Anastasia Bohsali
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303
| | - Rekha Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303
| | - Ihab Hajjar
- University of Texas Southwestern Dallas, TX 75390
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303
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10
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Ferré-González L, Balaguer Á, Roca M, Ftara A, Lloret A, Cháfer-Pericás C. Brain areas lipidomics in female transgenic mouse model of Alzheimer's disease. Sci Rep 2024; 14:870. [PMID: 38195731 PMCID: PMC10776612 DOI: 10.1038/s41598-024-51463-3] [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: 09/14/2023] [Accepted: 01/05/2024] [Indexed: 01/11/2024] Open
Abstract
Lipids are the major component of the brain with important structural and functional properties. Lipid disruption could play a relevant role in Alzheimer's disease (AD). Some brain lipidomic studies showed significant differences compared to controls, but few studies have focused on different brain areas related to AD. Furthermore, AD is more prevalent in females, but there is a lack of studies focusing on this sex. This work aims to perform a lipidomic study in selected brain areas (cerebellum, amygdala, hippocampus, entire cortex) from wild-type (WT, n = 10) and APPswe/PS1dE9 transgenic (TG, n = 10) female mice of 5 months of age, as a model of early AD, to identify alterations in lipid composition. A lipidomic mass spectrometry-based method was optimized and applied to brain tissue. As result, some lipids showed statistically significant differences between mice groups in cerebellum (n = 68), amygdala (n = 49), hippocampus (n = 48), and the cortex (n = 22). In addition, some lipids (n = 15) from the glycerolipid, phospholipid, and sphingolipid families were statistically significant in several brain areas simultaneously between WT and TG. A selection of lipid variables was made to develop a multivariate approach to assess their discriminant potential, showing high diagnostic indexes, especially in cerebellum and amygdala (sensitivity 70-100%, sensibility 80-100%).
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Affiliation(s)
- Laura Ferré-González
- Alzheimer's Disease Research Group, Health Research Institute La Fe, Avda de Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Ángel Balaguer
- Faculty of Mathematics, University of Valencia, Valencia, Spain
| | - Marta Roca
- Analytical Unit, Health Research Institute La Fe, Valencia, Spain
| | | | - Ana Lloret
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Valencia, Spain
| | - Consuelo Cháfer-Pericás
- Alzheimer's Disease Research Group, Health Research Institute La Fe, Avda de Fernando Abril Martorell, 106, 46026, Valencia, Spain.
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11
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Caballero HS, McFall GP, Gee M, MacDonald S, Phillips NA, Fogarty J, Montero-Odasso M, Camicioli R, Dixon RA. Cognitive Speed in Neurodegenerative Disease: Comparing Mean Rate and Inconsistency Within and Across the Alzheimer's and Lewy Body Spectra in the COMPASS-ND Study. J Alzheimers Dis 2024; 100:579-601. [PMID: 38875040 DOI: 10.3233/jad-240210] [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: 06/16/2024]
Abstract
Background Alzheimer's disease (AD) and Lewy body disease (LBD) are characterized by early and gradual worsening perturbations in speeded cognitive responses. Objective Using simple and choice reaction time tasks, we compared two indicators of cognitive speed within and across the AD and LBD spectra: mean rate (average reaction time across trials) and inconsistency (within person variability). Methods The AD spectrum cohorts included subjective cognitive impairment (SCI, n = 28), mild cognitive impairment (MCI, n = 121), and AD (n = 45) participants. The LBD spectrum included Parkinson's disease (PD, n = 32), mild cognitive impairment in PD (PD-MCI, n = 21), and LBD (n = 18) participants. A cognitively unimpaired (CU, n = 39) cohort served as common benchmark. We conducted multivariate analyses of variance and discrimination analyses. Results Within the AD spectrum, the AD cohort was slower and more inconsistent than the CU, SCI, and MCI cohorts. The MCI cohort was slower than the CU cohort. Within the LBD spectrum, the LBD cohort was slower and more inconsistent than the CU, PD, and PD-MCI cohorts. The PD-MCI cohort was slower than the CU and PD cohorts. In cross-spectra (corresponding cohort) comparisons, the LBD cohort was slower and more inconsistent than the AD cohort. The PD-MCI cohort was slower than the MCI cohort. Discrimination analyses clarified the group difference patterns. Conclusions For both speed tasks, mean rate and inconsistency demonstrated similar sensitivity to spectra-related comparisons. Both dementia cohorts were slower and more inconsistent than each of their respective non-dementia cohorts.
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Affiliation(s)
- H Sebastian Caballero
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - G Peggy McFall
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - Myrlene Gee
- Department of Medicine (Neurology), University of Alberta, Edmonton, AB, Canada
| | - Stuart MacDonald
- Department of Psychology, University of Victoria, Victoria, BC, Canada
| | | | | | | | - Richard Camicioli
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Department of Medicine (Neurology), University of Alberta, Edmonton, AB, Canada
| | - Roger A Dixon
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
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12
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Mousa D, Zayed N, Yassine IA. Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data. Sci Rep 2023; 13:21559. [PMID: 38057476 PMCID: PMC10700324 DOI: 10.1038/s41598-023-48693-2] [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: 03/26/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023] Open
Abstract
Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.
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Affiliation(s)
- Doaa Mousa
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt.
| | - Nourhan Zayed
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt
- Mechanical Engineering Department, The British University in Egypt, Cairo, Egypt
| | - Inas A Yassine
- Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt
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13
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Yang X, Wu H, Song Y, Chen S, Ge H, Yan Z, Yuan Q, Liang X, Lin X, Chen J. Functional MRI-specific alterations in frontoparietal network in mild cognitive impairment: an ALE meta-analysis. Front Aging Neurosci 2023; 15:1165908. [PMID: 37448688 PMCID: PMC10336325 DOI: 10.3389/fnagi.2023.1165908] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/16/2023] [Indexed: 07/15/2023] Open
Abstract
Background Mild cognitive impairment (MCI) depicts a transitory phase between healthy elderly and the onset of Alzheimer's disease (AD) with worsening cognitive impairment. Some functional MRI (fMRI) research indicated that the frontoparietal network (FPN) could be an essential part of the pathophysiological mechanism of MCI. However, damaged FPN regions were not consistently reported, especially their interactions with other brain networks. We assessed the fMRI-specific anomalies of the FPN in MCI by analyzing brain regions with functional alterations. Methods PubMed, Embase, and Web of Science were searched to screen neuroimaging studies exploring brain function alterations in the FPN in MCI using fMRI-related indexes, including the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity. We integrated distinctive coordinates by activating likelihood estimation, visualizing abnormal functional regions, and concluding functional alterations of the FPN. Results We selected 29 studies and found specific changes in some brain regions of the FPN. These included the bilateral dorsolateral prefrontal cortex, insula, precuneus cortex, anterior cingulate cortex, inferior parietal lobule, middle temporal gyrus, superior frontal gyrus, and parahippocampal gyrus. Any abnormal alterations in these regions depicted interactions between the FPN and other networks. Conclusion The study demonstrates specific fMRI neuroimaging alterations in brain regions of the FPN in MCI patients. This could provide a new perspective on identifying early-stage patients with targeted treatment programs. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432042, identifier: CRD42023432042.
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Affiliation(s)
- Xinyi Yang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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14
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Ballard HK, Jackson TB, Hicks TH, Cox SJ, Symm A, Maldonado T, Bernard JA. Hormone-sleep interactions predict cerebellar connectivity and behavior in aging females. Psychoneuroendocrinology 2023; 150:106034. [PMID: 36709633 PMCID: PMC10149037 DOI: 10.1016/j.psyneuen.2023.106034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/16/2022] [Accepted: 01/23/2023] [Indexed: 01/26/2023]
Abstract
Sex hormones fluctuate over the course of the female lifespan and are associated with brain health and cognition. Thus, hormonal changes throughout female adulthood, and with menopause in particular, may contribute to sex differences in brain function and behavior. Further, sex hormones have been correlated with sleep patterns, which also exhibit sex-specific impacts on the brain and behavior. As such, the interplay between hormones and sleep may contribute to late-life brain and behavioral outcomes in females. Here, in a sample of healthy adult females (n = 79, ages 35-86), we evaluated the effect of hormone-sleep interactions on cognitive and motor performance as well as cerebellar-frontal network connectivity. Salivary samples were used to measure 17β-estradiol, progesterone, and testosterone levels while overnight actigraphy was used to quantify sleep patterns. Cognitive behavior was quantified using the composite average of standardized scores on memory, processing speed, and attentional tasks, and motor behavior was indexed with sequence learning, balance, and dexterity tasks. We analyzed resting-state connectivity correlations for two specific cerebellar-frontal networks: a Crus I to dorsolateral prefrontal cortex network and a Lobule V to primary motor cortex network. In sum, results indicate that sex hormones and sleep patterns interact to predict cerebellar-frontal connectivity and behavior in aging females. Together, the current findings further highlight the potential consequences of endocrine aging in females and suggest that the link between sex hormones and sleep patterns may contribute, in part, to divergent outcomes between sexes in advanced age.
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Affiliation(s)
- Hannah K Ballard
- Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX, USA; Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA.
| | - T Bryan Jackson
- Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA
| | - Tracey H Hicks
- Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA
| | - Sydney J Cox
- Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA
| | - Abigail Symm
- Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA
| | - Ted Maldonado
- Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA; Department of Psychology, Indiana State University, Terre Haute, IN, USA
| | - Jessica A Bernard
- Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX, USA; Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA
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15
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Yin Z, Wang Z, Li Y, Zhou J, Chen Z, Xia M, Zhang X, Wu J, Zhao L, Liang F. Neuroimaging studies of acupuncture on Alzheimer's disease: a systematic review. BMC Complement Med Ther 2023; 23:63. [PMID: 36823586 PMCID: PMC9948384 DOI: 10.1186/s12906-023-03888-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 02/14/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Acupuncture effectively improves cognitive function in Alzheimer's disease (AD). Many neuroimaging studies have found significant brain alterations after acupuncture treatment of AD, but the underlying central modulation mechanism is unclear. OBJECTIVE This review aims to provide neuroimaging evidence to understand the central mechanisms of acupuncture in patients with AD. METHODS Relevant neuroimaging studies about acupuncture for AD were retrieved from eight English and Chinese medicine databases (PubMed, Embase, Cochrane Library, Web of Science, SinoMed, CNKI, WF, VIP) and other resources from inception of databases until June 1, 2022, and their methodological quality was assessed using RoB 2.0 and ROBINS - I. Brain neuroimaging information was extracted to investigate the potential neural mechanism of acupuncture for AD. Descriptive statistics were used for data analysis. RESULTS Thirteen neuroimaging studies involving 275 participants were included in this review, and the overall methodological quality of included studies was moderate. The approaches applied included task-state functional magnetic resonance imaging (ts-fMRI; n = 9 studies) and rest-state functional magnetic resonance imaging (rs-fMRI; n = 4 studies). All studies focused on the instant effect of acupuncture on the brains of AD participants, including the cingulate gyrus, middle frontal gyrus, and cerebellum, indicating that acupuncture may regulate the default mode, central executive, and frontoparietal networks. CONCLUSION This study provides evidence of the neural mechanisms underlying the effect of acupuncture on AD involving cognitive- and motor-associated networks. However, this evidence is still in the preliminary investigation stage. Large-scale, well-designed, multimodal neuroimaging trials are still required to provide comprehensive insight into the central mechanism underlying the effect of acupuncture on AD. (Systematic review registration at PROSPERO, No. CRD42022331527).
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Affiliation(s)
- Zihan Yin
- grid.411304.30000 0001 0376 205XSchool of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China ,Acupuncture Clinical Research Center of Sichuan Province, Chengdu, China
| | - Ziqi Wang
- grid.517561.1the Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Yaqin Li
- grid.411304.30000 0001 0376 205XSchool of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jun Zhou
- grid.411304.30000 0001 0376 205XSchool of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhenghong Chen
- grid.411304.30000 0001 0376 205XSchool of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China ,Acupuncture Clinical Research Center of Sichuan Province, Chengdu, China
| | - Manze Xia
- grid.411304.30000 0001 0376 205XSchool of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China ,Acupuncture Clinical Research Center of Sichuan Province, Chengdu, China
| | - Xinyue Zhang
- grid.411304.30000 0001 0376 205XSchool of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China ,Acupuncture Clinical Research Center of Sichuan Province, Chengdu, China
| | - Jiajing Wu
- grid.417409.f0000 0001 0240 6969School of Nursing, Zunyi Medical University, Zunyi, China
| | - Ling Zhao
- School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China. .,Acupuncture Clinical Research Center of Sichuan Province, Chengdu, China.
| | - Fanrong Liang
- School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China. .,Acupuncture Clinical Research Center of Sichuan Province, Chengdu, China.
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16
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Li H, Guan Q, Huang R, Lei M, Luo YJ, Zhang Z, Tao W. Altered functional coupling between the cerebellum and cerebrum in patients with amnestic mild cognitive impairment. Cereb Cortex 2023; 33:2061-2074. [PMID: 36857720 DOI: 10.1093/cercor/bhac193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 11/14/2022] Open
Abstract
Cognitive processing relies on the functional coupling between the cerebrum and cerebellum. However, it remains unclear how the 2 collaborate in amnestic mild cognitive impairment (aMCI) patients. With functional magnetic resonance imaging techniques, we compared cerebrocerebellar functional connectivity during the resting state (rsFC) between the aMCI and healthy control (HC) groups. Additionally, we distinguished coupling between functionally corresponding and noncorresponding areas across the cerebrum and cerebellum. The results demonstrated decreased rsFC between both functionally corresponding and noncorresponding areas, suggesting distributed deficits of cerebrocerebellar connections in aMCI patients. Increased rsFC was also observed, which were between functionally noncorresponding areas. Moreover, the increased rsFC was positively correlated with attentional scores in the aMCI group, and this effect was absent in the HC group, supporting that there exists a compensatory mechanism in patients. The current study contributes to illustrating how the cerebellum adjusts its coupling with the cerebrum in individuals with cognitive impairment.
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Affiliation(s)
- Hehui Li
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Rong Huang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Mengmeng Lei
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Yue-Jia Luo
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China.,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing 100875, P.R. China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing 100875, P.R. China
| | - Wuhai Tao
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
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17
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Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study. Brain Sci 2023; 13:brainsci13020265. [PMID: 36831808 PMCID: PMC9954618 DOI: 10.3390/brainsci13020265] [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: 12/24/2022] [Revised: 01/27/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.
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18
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Ji Y, Huang SQ, Cheng Q, Fu WW, Zhong PP, Chen XL, Shu BL, Wei B, Huang QY, Wu XR. Exploration of static functional connectivity and dynamic functional connectivity alterations in the primary visual cortex among patients with high myopia via seed-based functional connectivity analysis. Front Neurosci 2023; 17:1126262. [PMID: 36816124 PMCID: PMC9932907 DOI: 10.3389/fnins.2023.1126262] [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: 12/17/2022] [Accepted: 01/09/2023] [Indexed: 02/05/2023] Open
Abstract
Aim This study was conducted to explore differences in static functional connectivity (sFC) and dynamic functional connectivity (dFC) alteration patterns in the primary visual area (V1) among high myopia (HM) patients and healthy controls (HCs) via seed-based functional connectivity (FC) analysis. Methods Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 82 HM patients and 59 HCs who were closely matched for age, sex, and weight. Seed-based FC analysis was performed to identify alterations in the sFC and dFC patterns of the V1 in HM patients and HCs. Associations between mean sFC and dFC signal values and clinical symptoms in distinct brain areas among HM patients were identified via correlation analysis. Static and dynamic changes in brain activity in HM patients were investigated by assessments of sFC and dFC via calculation of the total time series mean and sliding-window analysis. Results In the left anterior cingulate gyrus (L-ACG)/left superior parietal gyrus (L-SPG) and left V1, sFC values were significantly greater in HM patients than in HCs. In the L-ACG and right V1, sFC values were also significantly greater in HM patients than in HCs [two-tailed, voxel-level P < 0.01, Gaussian random field (GRF) correction, cluster-level P < 0.05]. In the left calcarine cortex (L-CAL) and left V1, dFC values were significantly lower in HM patients than in HCs. In the right lingual gyrus (R-LING) and right V1, dFC values were also significantly lower in HM patients than in HCs (two-tailed, voxel-level P < 0.01, GRF correction, cluster-level P < 0.05). Conclusion Patients with HM exhibited significantly disturbed FC between the V1 and various brain regions, including L-ACG, L-SPG, L-CAL, and R-LING. This disturbance suggests that patients with HM could exhibit impaired cognitive and emotional processing functions, top-down control of visual attention, and visual information processing functions. HM patients and HCs could be distinguished from each other with high accuracy using sFC and dFC variabilities. These findings may help to identify the neural mechanism of decreased visual performance in HM patients.
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Montalà-Flaquer M, Cañete-Massé C, Vaqué-Alcázar L, Bartrés-Faz D, Peró-Cebollero M, Guàrdia-Olmos J. Spontaneous brain activity in healthy aging: An overview through fluctuations and regional homogeneity. Front Aging Neurosci 2023; 14:1002811. [PMID: 36711210 PMCID: PMC9877451 DOI: 10.3389/fnagi.2022.1002811] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/23/2022] [Indexed: 01/15/2023] Open
Abstract
Introduction This study aims to explore whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences among age groups within a population ranging from middle age to older adults. Methods The sample comprised 112 healthy persons (M = 68.80, SD = 7.99) aged 48-89 who were split into six age groups (< 60, 60-64, 65-69, 70-74, 75-79, and ≥ 80). Fractional amplitude of low-frequency fluctuation and ReHo analyses were performed and were compared among the six age groups, and the significant results commonly found across groups were correlated with the gray matter volume of the areas and the age variable. Results Increased activity was found using fALFF in the superior temporal gyrus and inferior frontal gyrus when comparing the first group and the fifth. Regarding ReHo analysis, Group 6 showed increased ReHo in the temporal lobe (hippocampus), right and left precuneus, right caudate, and right and left thalamus depending on the age group. Moreover, significant correlations between age and fALFF and ReHo clusters, as well as with their gray matter volume were found, meaning that the higher the age, the higher the regional synchronization, the lower the fALFF activation, and the lower gray matter of the right thalamus. Conclusion Both techniques have been shown to be valuable and usable tools for disentangling brain changes in activation in a very low interval of years in healthy aging.
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Affiliation(s)
- Marc Montalà-Flaquer
- Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, Universitat de Barcelona, Barcelona, Spain,UB Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain,*Correspondence: Marc Montalà-Flaquer,
| | - Cristina Cañete-Massé
- Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, Universitat de Barcelona, Barcelona, Spain,UB Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain
| | - Lídia Vaqué-Alcázar
- Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain,Department of Medicine, Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain,Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - David Bartrés-Faz
- Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain,Department of Medicine, Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain,Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Maribel Peró-Cebollero
- Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, Universitat de Barcelona, Barcelona, Spain,UB Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain,Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, Universitat de Barcelona, Barcelona, Spain,UB Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain,Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain
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20
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Kannan L, Bhatt T, Ajilore O. Cerebello-cortical functional connectivity may regulate reactive balance control in older adults with mild cognitive impairment. Front Neurol 2023; 14:1041434. [PMID: 37139074 PMCID: PMC10149739 DOI: 10.3389/fneur.2023.1041434] [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: 09/10/2022] [Accepted: 03/20/2023] [Indexed: 05/05/2023] Open
Abstract
Background Older adults with mild cognitive impairment (OAwMCI) experience a two-fold increased risk of falling compared to their cognitively intact counterparts. This increased risk could be attributed to impairments in balance control mechanisms (both volitional and reactive), however, the exact neural substrates contributing to the balance impairments remain unclear. While changes in functional connectivity (FC) networks in volitional balance control tasks have been well highlighted, the relationship between these changes and reactive balance control has not been examined. Therefore, this study aims to explore the relationship between FC networks of the brain obtained during resting state fMRI (no visualization or active task performed) and behavioral measures on a reactive balance task in OAwMCI. Methods Eleven OAwMCI (< 25/30 on MoCA, > 55 years) underwent fMRI and were exposed to slip-like perturbations on the Activestep treadmill. Postural stability, i.e., dynamic center of mass motion state (i.e., its position and velocity) was computed to determine reactive balance control performance. The relationship between reactive stability and FC networks was explored using the CONN software. Results OAwMCI with greater FC in default mode network-cerebellum (r2 = 0.43, p < 0.05), and sensorimotor-cerebellum (r2 = 0.41, p < 0.05) network exhibited lower reactive stability. Further, people with lower FC in middle frontal gyrus-cerebellum (r2 = 0.37, p < 0.05), frontoparietal-cerebellum (r2 = 0.79, p < 0.05) and cerebellar network-brainstem (r2 = 0.49, p < 0.05) exhibited lower reactive stability. Conclusion Older adults with mild cognitive impairment demonstrate significant associations between reactive balance control and cortico-subcortical regions involved in cognitive-motor control. Results indicate that the cerebellum and its communications with higher cortical centers could be potential substrates contributing to impaired reactive responses in OAwMCI.
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Affiliation(s)
- Lakshmi Kannan
- Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL, United States
| | - Tanvi Bhatt
- Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL, United States
- *Correspondence: Tanvi Bhatt
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
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21
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Srivishagan S, Kumaralingam L, Thanikasalam K, Pinidiyaarachchi UAJ, Ratnarajah N. Discriminative patterns of white matter changes in Alzheimer's. Psychiatry Res Neuroimaging 2023; 328:111576. [PMID: 36495726 DOI: 10.1016/j.pscychresns.2022.111576] [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: 09/08/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 12/02/2022]
Abstract
Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres. The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.
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Affiliation(s)
- Subaramya Srivishagan
- Department of Physical Science, Faculty of Applied Science, University of Vavuniya, Vavuniya, Sri Lanka; PGIS, University of Peradeniya, Peradeniya, Sri Lanka
| | - Logiraj Kumaralingam
- Department of Computer Science, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
| | - Kokul Thanikasalam
- Department of Computer Science, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
| | - U A J Pinidiyaarachchi
- Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka
| | - Nagulan Ratnarajah
- Department of Physical Science, Faculty of Applied Science, University of Vavuniya, Vavuniya, Sri Lanka.
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22
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Liu X, Zeng Q, Luo X, Li K, Xu X, Hong L, Li J, Guan X, Xu X, Huang P, Zhang M. Effects of APOE ε2 allele on basal forebrain functional connectivity in mild cognitive impairment. CNS Neurosci Ther 2022; 29:597-608. [PMID: 36468416 PMCID: PMC9873529 DOI: 10.1111/cns.14038] [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: 06/09/2022] [Revised: 10/27/2022] [Accepted: 11/10/2022] [Indexed: 12/10/2022] Open
Abstract
BACKGROUND Basal forebrain cholinergic system (BFCS) dysfunction is associated with cognitive decline in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Apolipoprotein E (APOE) ε2 is a protective genetic factor in AD and MCI, and cholinergic sprouting depends on APOE. OBJECTIVE We investigated the effect of the APOE ε2 allele on BFCS functional connectivity (FC) in cognitively normal (CN) subjects and MCI patients. METHOD We included 60 MCI patients with APOE ε3/ε3, 18 MCI patients with APOE ε2/ε3, 73 CN subjects with APOE ε3/ε3, and 36 CN subjects with APOE ε2/ε3 genotypes who had resting-state functional magnetic resonance imaging data from the Alzheimer's disease Neuroimaging Initiative. We used BFCS subregions (Ch1-3 and Ch4) as seeds and calculated the FC with other brain areas. Using a mixed-effect analysis, we explored the interaction effects of APOE ε2 allele × cognitive status on BFCS-FC. Furthermore, we examined the relationships between imaging metrics, cognitive abilities, and AD pathology markers, controlling for sex, age, and education as covariates. RESULTS An interaction effect on functional connectivity was found between the right Ch4 (RCh4) and left insula (p < 0.05, corrected), and between the RCh4 and left Rolandic operculum (p < 0.05, corrected). Among all subjects and APOE ε2 carriers, RCh4-left Insula FC was associated with early tau deposition. Furthermore, no correlation was found between imaging metrics and amyloid burden. Among all subjects and APOE ε2 carriers, FC metrics were associated with cognitive performance. CONCLUSION The APOE ε2 genotype may play a protective role during BFCS degeneration in MCI.
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Affiliation(s)
- Xiaocao Liu
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Qingze Zeng
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiao Luo
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Kaicheng Li
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaopei Xu
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Luwei Hong
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Jixuan Li
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaojun Guan
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaojun Xu
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Peiyu Huang
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Min‐Ming Zhang
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
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23
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Effects of apolipoprotein E4 genotype on cerebro-cerebellar connectivity, brain atrophy, and cognition in patients with Alzheimer's disease. J Neurol Sci 2022; 442:120435. [PMID: 36201963 DOI: 10.1016/j.jns.2022.120435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 08/28/2022] [Accepted: 09/18/2022] [Indexed: 11/20/2022]
Abstract
INTRODUCTION While several studies have substantially revealed the influence of the apolipoprotein E4 genotype (APOE4) on the vulnerability of Alzheimer's disease (AD), there are still far fewer studies investigating whether and how APOE4, in the absence of the amyloid-β (Aβ), alters regional brain atrophy, cerebro-cerebellar connectivity and cognitive performance in AD patients. METHODS We employed MRI and neuropsychological data from 234 old adults with AD dementia, including 143 APOE4-positive (with ε2/ε4, ε3/ε4, or ε4/ε4 alleles) and 91 APOE4-negative (with ε2/ε2, ε2/ε3 or ε3/ε3), to investigate the cerebro-cerebellar connectivity in three cerebro-cerebellar brain networks: default mode network, motor network and affective-limbic network. Amyloid PET images were used to evaluate individual Aβ burdens, later used as covariates. Regional volumetric and cortical thickness measures were quantified in both the cerebellum and the cerebrum using the cerebellum segmentation algorithm and Freesurfer5.3, respectively. RESULTS Our corrected functional connectivity (FC) results showed that APOE4 carriers (APOE4+) had lower FC within the cerebro-cerebellar motor network. In addition, significant group differences in regional cortical thickness were observed in the left Crus I, the right VIIB, left superior frontal, and right middle temporal gyri. Group differences in regional brain volumes were observed in the left lobule V and right parstriangularis. Furthermore, multiple linear regression analysis indicated that APOE4+ AD patients show greater episodic memory impairment. CONCLUSION Since amyloid-β, age, education, and gender were included as confounds in the statistical models, our findings suggest that APOE4 independently contributes to brain atrophy, disrupted FC, and associated memory declines in AD patients.
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24
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Mohammadian F, Zare Sadeghi A, Noroozian M, Malekian V, Abbasi Sisara M, Hashemi H, Mobarak Salari H, Valizadeh G, Samadi F, Sodaei F, Saligheh Rad H. Quantitative Assessment of Resting-State Functional Connectivity MRI to Differentiate Amnestic Mild Cognitive Impairment, Late-Onset Alzheimer's Disease From Normal Subjects. J Magn Reson Imaging 2022; 57:1702-1712. [PMID: 36226735 DOI: 10.1002/jmri.28469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/25/2022] [Accepted: 09/27/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting-state functional MRI (rs-fMRI) can provide valuable information about the brain network pattern in early AD diagnosis. PURPOSE To quantitatively assess FC patterns of resting-state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late-onset AD from normal. STUDY TYPE Prospective. SUBJECTS A total of 14 normal, 16 aMCI, and 13 late-onset AD. FIELD STRENGTH/SEQUENCE A 3.0 T; rs-fMRI: single-shot 2D-EPI and T1-weighted structure: MPRAGE. ASSESSMENT By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI-to-ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs. STATISTICAL TESTS Region of interest (ROI)-based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)-corrected P < 0.05 cluster-level threshold together with posthoc uncorrected P < 0.05 connection-level threshold. Graph-theory analysis (GTA): P-FDR-corrected < 0.05. One-way ANOVA and Chi-square tests were used to compare clinical characteristics. RESULTS PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global-efficiency (28.05 < 45), local-efficiency (22.98 < 24.05), and betweenness-centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local-efficiency (33.46 > 24.05) and clustering-coefficient (25 > 20.18) were found in aMCI compared to normal. DATA CONCLUSION This study demonstrated resting-state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Fatemeh Mohammadian
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.,Quantitative Medical Imaging/Spectroscopy Group, Tehran University of Medical Science, Tehran, Iran
| | - Arash Zare Sadeghi
- Medical Physics Department, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Noroozian
- Department of Psychiatry, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahid Malekian
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Majid Abbasi Sisara
- Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Hasan Hashemi
- Department of Radiology, Advanced Diagnostic and Interventional Radiology Research Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hanieh Mobarak Salari
- Quantitative Medical Imaging/Spectroscopy Group, Tehran University of Medical Science, Tehran, Iran
| | - Gelareh Valizadeh
- Quantitative Medical Imaging/Spectroscopy Group, Tehran University of Medical Science, Tehran, Iran
| | - Fardin Samadi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Forough Sodaei
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.,Quantitative Medical Imaging/Spectroscopy Group, Tehran University of Medical Science, Tehran, Iran
| | - Hamidreza Saligheh Rad
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.,Quantitative Medical Imaging/Spectroscopy Group, Tehran University of Medical Science, Tehran, Iran
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25
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Yao Q, Tang F, Wang Y, Yan Y, Dong L, Wang T, Zhu D, Tian M, Lin X, Shi J. Effect of cerebellum stimulation on cognitive recovery in patients with Alzheimer disease: A randomized clinical trial. Brain Stimul 2022; 15:910-920. [PMID: 35700915 DOI: 10.1016/j.brs.2022.06.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/01/2022] [Accepted: 06/05/2022] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Evidence indicates that the cerebellum is involved in cognitive processing. However, the specific mechanisms through which the cerebellum repetitive transcranial magnetic stimulation (rTMS) contributes to the cognitive state are unclear. METHODS In the current randomized, double-blind, sham-controlled trial, 27 patients with Alzheimer's disease (AD) were randomly allotted to one of the two groups: rTMS-real or rTMS-sham. We investigated the efficacy of a four-week treatment of bilateral cerebellum rTMS to promote cognitive recovery and alter specific cerebello-cerebral functional connectivity. RESULTS The cerebellum rTMS significantly improves multi-domain cognitive functions, directly associated with the observed intrinsic functional connectivity between the cerebellum nodes and the dorsolateral prefrontal cortex (DLPFC), medial frontal cortex, and the cingulate cortex in the real rTMS group. In contrast, the sham stimulation showed no significant impact on the clinical improvements and the cerebello-cerebral connectivity. CONCLUSION Our results depict that 5 Hz rTMS of the bilateral cerebellum is a promising, non-invasive treatment of cognitive dysfunction in AD patients. This cognitive improvement is accompanied by brain connectivity modulation and is consistent with the pathophysiological brain disconnection model in AD patients.
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Affiliation(s)
- Qun Yao
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Fanyu Tang
- Department of Neurology, The Second People's Hospital of Bengbu, Bengbu, Anhui, China.
| | - Yingying Wang
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Yixin Yan
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Lin Dong
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Tong Wang
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Donglin Zhu
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Minjie Tian
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Xingjian Lin
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Jingping Shi
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
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26
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Bernard JA. Don't forget the little brain: A framework for incorporating the cerebellum into the understanding of cognitive aging. Neurosci Biobehav Rev 2022; 137:104639. [PMID: 35346747 PMCID: PMC9119942 DOI: 10.1016/j.neubiorev.2022.104639] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/28/2022] [Accepted: 03/23/2022] [Indexed: 12/22/2022]
Abstract
With the rapidly growing population of older adults, an improved understanding of brain and cognitive aging is critical, given the impacts on health, independence, and quality of life. To this point, we have a well-developed literature on the cortical contributions to cognition in advanced age. However, while this work has been foundational for our understanding of brain and behavior in older adults, subcortical contributions, particularly those from the cerebellum, have not been integrated into these models and frameworks. Incorporating the cerebellum into models of cognitive aging is an important step for moving the field forward. There has also been recent interest in this structure in Alzheimer's dementia, indicating that such work may be beneficial to our understanding of neurodegenerative disease. Here, I provide an updated overview of the cerebellum in advanced age and propose that it serves as a critical source of scaffolding or reserve for cortical function. Age-related impacts on cerebellar function further impact cortical processing, perhaps resulting in many of the activation patterns commonly seen in aging.
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Affiliation(s)
- Jessica A Bernard
- Department of Psychological and Brain Sciences, USA; Texas A&M Institute for Neuroscience, Texas A&M University, USA.
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27
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Chau ACM, Smith AE, Hordacre B, Kumar S, Cheung EYW, Mak HKF. A scoping review of resting-state brain functional alterations in Type 2 diabetes. Front Neuroendocrinol 2022; 65:100970. [PMID: 34922997 DOI: 10.1016/j.yfrne.2021.100970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/18/2021] [Accepted: 12/07/2021] [Indexed: 11/28/2022]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been actively used in the last decade to investigate brain functional connectivity alterations in Type 2 Diabetes Mellitus (T2DM) to understand the neuropathophysiology of T2DM in cognitive degeneration. Given the emergence of new analysis techniques, this scoping review aims to map the rs-fMRI analysis techniques that have been applied in the literature and reports the latest rs-fMRI findings that have not been covered in previous reviews. Graph theory, the contemporary rs-fMRI analysis, has been used to demonstrate altered brain topological organisations in people with T2DM, which included altered degree centrality, functional connectivity strength, the small-world architecture and network-based statistics. These alterations were correlated with T2DM patients' cognitive performances. Graph theory also contributes to identify unbiased seeds for seed-based analysis. The expanding rs-fMRI analytical approaches continue to provide new evidence that helps to understand the mechanisms of T2DM-related cognitive degeneration.
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Affiliation(s)
- Anson C M Chau
- Medical Imaging, Medical Radiation Science, Allied Health and Human Performance, University of South Australia, Adelaide, Australia; Alliance for Research in Exercise, Nutrition and Activity (ARENA), Allied Health and Human Performance, University of South Australia, Adelaide, Australia.
| | - Ashleigh E Smith
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), Allied Health and Human Performance, University of South Australia, Adelaide, Australia.
| | - Brenton Hordacre
- IIMPACT in Health, Allied Health and Human Performance, University of South Australia, Adelaide, Australia.
| | - Saravana Kumar
- IIMPACT in Health, Allied Health and Human Performance, University of South Australia, Adelaide, Australia; Allied Health and Human Performance, University of South Australia, Adelaide, Australia.
| | - Eva Y W Cheung
- School of Medical and Health Sciences, Tung Wah College, Hong Kong.
| | - Henry K F Mak
- Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong; Alzheimer's Disease Research Network, The University of Hong Kong, Hong Kong; State Key Laboratory for Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong.
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28
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Yang Z, Sheng X, Qin R, Chen H, Shao P, Xu H, Yao W, Zhao H, Xu Y, Bai F. Cognitive Improvement via Left Angular Gyrus-Navigated Repetitive Transcranial Magnetic Stimulation Inducing the Neuroplasticity of Thalamic System in Amnesic Mild Cognitive Impairment Patients. J Alzheimers Dis 2022; 86:537-551. [PMID: 35068464 DOI: 10.3233/jad-215390] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Stimulating superficial brain regions highly associated with the hippocampus by repetitive transcranial magnetic stimulation (rTMS) may improve memory of Alzheimer’s disease (AD) spectrum patients. Objective: We recruited 16 amnesic mild cognitive impairment (aMCI) and 6 AD patients in the study. All the patients were stimulated to the left angular gyrus, which was confirmed a strong link to the hippocampus through neuroimaging studies, by the neuro-navigated rTMS for four weeks. Methods: Automated fiber quantification using diffusion tensor imaging metrics and graph theory analysis on functional network were employed to detect the neuroplasticity of brain networks. Results: After neuro-navigated rTMS intervention, the episodic memory of aMCI patients and Montreal Cognitive Assessment score of two groups were significantly improved. Increased FA values of right anterior thalamic radiation among aMCI patients, while decreased functional network properties of thalamus subregions were observed, whereas similar changes not found in AD patients. It is worth noting that the improvement of cognition was associated with the neuroplasticity of thalamic system. Conclusion: We speculated that the rTMS intervention targeting left angular gyrus may be served as a strategy to improve cognitive impairment at the early stage of AD patients, supporting by the neuroplasticity of thalamic system.
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Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Hengheng Xu
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Weina Yao
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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Hu Q, Wang Q, Li Y, Xie Z, Lin X, Huang G, Zhan L, Jia X, Zhao X. Intrinsic Brain Activity Alterations in Patients With Mild Cognitive Impairment-to-Normal Reversion: A Resting-State Functional Magnetic Resonance Imaging Study From Voxel to Whole-Brain Level. Front Aging Neurosci 2022; 13:788765. [PMID: 35111039 PMCID: PMC8802752 DOI: 10.3389/fnagi.2021.788765] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 12/08/2021] [Indexed: 12/25/2022] Open
Abstract
Mild cognitive impairment (MCI) reversion refers to patients with MCI who revert from MCI to a normal cognitive state. Exploring the underlying neuromechanism of MCI reverters may contribute to providing new insights into the pathogenesis of Alzheimer's disease and developing therapeutic interventions. Information on patients with MCI and healthy controls (HCs) was collected from the Alzheimer's Disease Neuroimaging Initiative database. We redefined MCI reverters as patients with MCI whose logical memory scores changed from MCI to normal levels using the logical memory criteria. We explored intrinsic brain activity alterations in MCI reverters from voxel, regional, and whole-brain levels by comparing resting-state functional magnetic resonance imaging metrics of the amplitude of low-frequency of fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) between MCI reverters and HCs. Finally, partial correlation analyses were conducted between cognitive scale scores and resting-state functional magnetic resonance imaging metrics of brain regions, revealing significant group differences. Thirty-two patients with MCI from the Alzheimer's Disease Neuroimaging Initiative database were identified as reverters. Thirty-seven age-, sex-, and education-matched healthy individuals were also enrolled. At the voxel level, compared with the HCs, MCI reverters had increased ALFF, fALFF, and PerAF in the frontal gyrus (including the bilateral orbital inferior frontal gyrus and left middle frontal gyrus), increased PerAF in the left fusiform gyrus, and decreased ALFF and fALFF in the right inferior cerebellum. Regarding regional and whole-brain levels, MCI reverters showed increased ReHo in the left fusiform gyrus and right median cingulate and paracingulate gyri; increased DC in the left inferior temporal gyrus and left medial superior frontal; decreased DC in the right inferior cerebellum and bilateral insular gyrus relative to HCs. Furthermore, significant correlations were found between cognitive performance and neuroimaging changes. These findings suggest that MCI reverters show significant intrinsic brain activity changes compared with HCs, potentially related to the cognitive reversion of patients with MCI. These results enhance our understanding of the underlying neuromechanism of MCI reverters and may contribute to further exploration of Alzheimer's disease.
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Affiliation(s)
- Qili Hu
- Department of Radiology, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
| | - Qianqian Wang
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yunfei Li
- Department of Radiology, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
| | - Zhou Xie
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Xiaomei Lin
- Department of Radiology, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
| | - Guofeng Huang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - LinLin Zhan
- School of Western Language, Heilongjiang University, Heilongjiang, China
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Xiaohu Zhao
- Department of Radiology, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
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30
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Jiang Z, Cai Y, Zhang X, Lv Y, Zhang M, Li S, Lin G, Bao Z, Liu S, Gu W. Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning. Front Aging Neurosci 2021; 13:715517. [PMID: 34867266 PMCID: PMC8633536 DOI: 10.3389/fnagi.2021.715517] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/25/2021] [Indexed: 01/14/2023] Open
Abstract
Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR. Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).
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Affiliation(s)
- Zhaoshun Jiang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Yuxi Cai
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Xixue Zhang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Yating Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Mengting Zhang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhijun Bao
- Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China.,Department of Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Research Center on Aging and Medicine, Fudan University, Shanghai, China
| | - Songbin Liu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Weidong Gu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
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Sun J, Zeng H, Pan L, Wang X, Liu M. Acupressure and Cognitive Training Can Improve Cognitive Functions of Older Adults With Mild Cognitive Impairment: A Randomized Controlled Trial. Front Psychol 2021; 12:726083. [PMID: 34867607 PMCID: PMC8635488 DOI: 10.3389/fpsyg.2021.726083] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 10/20/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Given the limited effectiveness of pharmacological treatments in mitigating cognitive decline in individuals with mild cognitive impairment (MCI), there is a pressing need for developing effective non-pharmacological intervention programs to counteract MCI-related cognitive decline. Acupressure and cognitive training are safe and cost-effective; however, evidence of the effect of acupressure or the combined effect of acupressure and cognitive training on cognitive functions of older adults with MCI is limited. Objective: To evaluate both the individual and combined effects of acupressure and cognitive training on cognitive functions of older adults with MCI. Methods: One hundred and eighty older adults with MCI were recruited and randomly assigned to combined acupressure and cognitive training group (n = 45), acupressure group (n = 45), cognitive training group (n = 45), or control group (n = 45). Participants in the experimental groups received self-administered and group-based training sessions, while those in the control group received routine community education. The intervention lasted for 6 months. The cognitive functions of all the participants were assessed at multiple stages, including pre-intervention, at the end of the third and sixth months. Results: One hundred and fifty-one participants completed the study, and all participants analyzed in intervention groups completed at least 85% of all practice sessions recommended. Repeated measures analysis of variance of the scores of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) at different time points among the four groups revealed that the group effect, time effect, and interaction effect were all significant (p < 0.01). Pairwise comparisons with Bonferroni correction showed that the scores of MMSE and MoCA in acupressure group, cognitive training group, and combined group were significantly raised compared with control group (p < 0.01). Compared with acupressure or cognitive training groups, the scores of MMSE and MoCA in combined group were significantly higher (p < 0.05). The scores of MMSE and MoCA in acupressure group had no significant differences with those in cognitive training group (p > 0.05). Conclusion: Acupressure and cognitive training both could improve the cognitive functions of older adults with MCI, and when used together, the effects were enhanced. Clinical Trial Registration: This study was registered in the Chinese Clinical Trial Registry (No.ChiCTR2100049955).
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Affiliation(s)
- Jingxian Sun
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hui Zeng
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Lu Pan
- Second Xiangya Hospital, Central South University, Changsha, China
| | | | - Mengjiao Liu
- Second Xiangya Hospital, Central South University, Changsha, China
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