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Chen B, Chen X, Peng L, Liu S, Tang Y, Gao X. Metabolic network connectivity disturbances in Parkinson's disease: a novel imaging biomarker. Cereb Cortex 2024; 34:bhae355. [PMID: 39329355 DOI: 10.1093/cercor/bhae355] [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: 04/15/2024] [Revised: 07/20/2024] [Accepted: 08/14/2024] [Indexed: 09/28/2024] Open
Abstract
The diagnosis of Parkinson's Disease (PD) presents ongoing challenges. Advances in imaging techniques like 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have highlighted metabolic alterations in PD, yet the dynamic network interactions within the metabolic connectome remain elusive. To this end, we examined a dataset comprising 49 PD patients and 49 healthy controls. By employing a personalized metabolic connectome approach, we assessed both within- and between-network connectivities using Standard Uptake Value (SUV) and Jensen-Shannon Divergence Similarity Estimation (JSSE). A random forest algorithm was utilized to pinpoint key neuroimaging features differentiating PD from healthy states. Specifically, the results revealed heightened internetwork connectivity in PD, specifically within the somatomotor (SMN) and frontoparietal (FPN) networks, persisting after multiple comparison corrections (P < 0.05, Bonferroni adjusted for 10% and 20% sparsity). This altered connectivity effectively distinguished PD patients from healthy individuals. Notably, this study utilizes 18F-FDG PET imaging to map individual metabolic networks, revealing enhanced connectivity in the SMN and FPN among PD patients. This enhanced connectivity may serve as a promising imaging biomarker, offering a valuable asset for early PD detection.
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Affiliation(s)
- Bei Chen
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, No. 172, Tongzipo Road, Changsha City, Hunan Province, Changsha 410008, China
| | - Xiran Chen
- College of Mathematics and Statistics, Chongqing Jiaotong University, Xuefu Road No. 66, Chongqing 400074, China
| | - Liling Peng
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Guilin Road No. 406, Shanghai 200233 China
- Hubei Province Key Laboratory of Molecular Imaging, Jiefang Road No. 1277, Wuhan 430022 China
| | - Shiqi Liu
- College of Mathematics and Statistics, Chongqing Jiaotong University, Xuefu Road No. 66, Chongqing 400074, China
| | - Yongxiang Tang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, No. 172, Tongzipo Road, Changsha City, Hunan Province, Changsha 410008, China
| | - Xin Gao
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Guilin Road No. 406, Shanghai 200233 China
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Zhou W, Zhu B, Weng Y, Chen C, Ni J, Shen W, Lan W, Wang J. The Combination of Presurgical Cortical Gray Matter Volumetry and Cerebral Perfusion Improves the Efficacy of Predicting Postoperative Cognitive Impairment of Elderly Patients. Tomography 2024; 10:1379-1396. [PMID: 39330750 PMCID: PMC11435822 DOI: 10.3390/tomography10090104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 08/23/2024] [Accepted: 08/28/2024] [Indexed: 09/28/2024] Open
Abstract
BACKGROUND Postoperative cognitive dysfunction (POCD) is a common complication of the central nervous system in elderly surgical patients. Structural MRI and arterial spin labelling (ASL) techniques found that the grey matter volume and cerebral perfusion in some specific brain areas are associated with the occurrence of POCD, but the results are inconsistent, and the predictive accuracy is low. We hypothesised that the combination of cortical grey matter volumetry and cerebral blood flow yield higher accuracy than either of the methods in discriminating the elderly individuals who are susceptible to POCD after abdominal surgery. MATERIALS AND METHODS Participants underwent neuropsychological testing before and after surgery. Postoperative cognitive dysfunction (POCD) was defined as a decrease in cognitive score of at least 20%. ASL-MRI and T1-weighted imaging were performed before surgery. We compared differences in cerebral blood flow (CBF) and cortical grey matter characteristics between POCD and non-POCD patients and generated receiver operating characteristic curves. RESULTS Out of 51 patients, 9 (17%) were diagnosed with POCD. CBF in the inferior frontal gyrus was lower in the POCD group compared to the non-POCD group (p < 0.001), and the volume of cortical grey matter in the anterior cingulate gyrus was higher in the POCD group (p < 0.001). The highest AUC value was 0.973. CONCLUSIONS The combination of cortical grey matter volumetry and cerebral perfusion based on ASL-MRI has improved efficacy in the early warning of POCD to elderly abdominal surgical patients.
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Affiliation(s)
- Weijian Zhou
- Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, China
- Health Science Centre, Ningbo University, Ningbo 315211, China
| | - Binbin Zhu
- Department of Anaesthesiology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, China
- Health Science Centre, Ningbo University, Ningbo 315211, China
| | - Yifei Weng
- Department of Radiology, The First Affiliated Hospital of Xiamen University, 55 Zhenhai Road, Siming District, Xiamen 361026, China
| | - Chunqu Chen
- Health Science Centre, Ningbo University, Ningbo 315211, China
| | - Jiajing Ni
- Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, China
- Health Science Centre, Ningbo University, Ningbo 315211, China
| | - Wenqi Shen
- Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, China
- Health Science Centre, Ningbo University, Ningbo 315211, China
| | - Wenting Lan
- Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, China
| | - Jianhua Wang
- Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, China
- Department of Radiology, The First Affiliated Hospital of Xiamen University, 55 Zhenhai Road, Siming District, Xiamen 361026, China
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Li X, Pang H, Bu S, Zhao M, Wang J, Liu Y, Yu H, Fan G. Stage-dependent differential impact of network communication on cognitive function across the continuum of cognitive decline in Parkinson's disease. Neurobiol Dis 2024; 199:106578. [PMID: 38925316 DOI: 10.1016/j.nbd.2024.106578] [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: 04/02/2024] [Revised: 06/04/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE Our objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how the influences of individual-level network connections on cognition varied across clinical stages without assuming a constant relationship. METHODS 108 PD patients with continuum of cognitive decline (PD-NC = 46, PD-MCI = 43, PDD = 19) and 34 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological tests. Independent component analysis (ICA) and graph theory analyses (GTA) were employed to explore RSN connection changes. Additionally, stage-dependent differential impact of network communication on cognitive performance were examined using sparse varying coefficient modeling. RESULTS Compared to HCs, the dorsal attention network (DAN) and dorsal sensorimotor network (dSMN) were central networks with decreased connections in PD-NC and PD-MCI stage, while the lateral visual network (LVN) emerged as a central network in patients with dementia. Additionally, connectivity of the cerebellum network (CBN) increased in the PD-NC and PD-MCI stages. GTA demonstrated decreased nodal metrics for DAN and dSMN, coupled with an increase for CBN. Moreover, the degree centrality (DC) values of DAN and dSMN exhibited a stage-dependent differential impact on cognitive performance across the continuum of cognitive decline. CONCLUSION Our findings suggest that across the progression of cognitive impairment, the LVN gradually transitions into a core node with reduced connectivity, while the enhancement of connections in CBN diminishes. Furthermore, the non-linear relationship between the DC values of RSNs and cognitive decline indicates the potential for tailored interventions targeting specific stages.
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Affiliation(s)
- Xiaolu Li
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Huize Pang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Shuting Bu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Mengwan Zhao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Juzhou Wang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Yu Liu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Hongmei Yu
- Department of Neurology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China.
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Altham C, Zhang H, Pereira E. Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review. PLoS One 2024; 19:e0303644. [PMID: 38753740 PMCID: PMC11098383 DOI: 10.1371/journal.pone.0303644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. METHODS To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. RESULTS Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. CONCLUSIONS Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.
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Affiliation(s)
- Callum Altham
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Huaizhong Zhang
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Ella Pereira
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
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Chen P, Tang G, Wang Y, Xiong W, Deng Y, Fei S, Zhang J. Spontaneous brain activity in the hippocampal regions could characterize cognitive impairment in patients with Parkinson's disease. CNS Neurosci Ther 2024; 30:e14706. [PMID: 38584347 PMCID: PMC10999557 DOI: 10.1111/cns.14706] [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: 11/12/2023] [Revised: 02/19/2024] [Accepted: 03/15/2024] [Indexed: 04/09/2024] Open
Abstract
OBJECTIVE This study aimed to investigate whether spontaneous brain activity can be used as a prospective indicator to identify cognitive impairment in patients with Parkinson's disease (PD). METHODS Resting-state functional magnetic resonance imaging (RS-fMRI) was performed on PD patients. The cognitive level of patients was assessed by the Montreal Cognitive Assessment (MoCA) scale. The fractional amplitude of low-frequency fluctuation (fALFF) was applied to measure the strength of spontaneous brain activity. Correlation analysis and between-group comparisons of fMRI data were conducted using Rest 1.8. By overlaying cognitively characterized brain regions and defining regions of interest (ROIs) based on their spatial distribution for subsequent cognitive stratification studies. RESULTS A total of 58 PD patients were enrolled in this study. They were divided into three groups: normal cognition (NC) group (27 patients, average MoCA was 27.96), mild cognitive impairment (MCI) group (21 patients, average MoCA was 23.52), and severe cognitive impairment (SCI) group (10 patients, average MoCA was 17.3). It is noteworthy to mention that those within the SCI group exhibited the most advanced chronological age, with an average of 74.4 years, whereas the MCI group displayed a higher prevalence of male participants at 85.7%. It was found hippocampal regions were a stable representative brain region of cognition according to the correlation analysis between the fALFF of the whole brain and cognition, and the comparison of fALFF between different cognitive groups. The parahippocampal gyrus was the only region with statistically significant differences in fALFF among the three cognitive groups, and it was also the only brain region to identify MCI from NC, with an AUC of 0.673. The paracentral lobule, postcentral gyrus was the region that identified SCI from NC, with an AUC of 0.941. The midbrain, hippocampus, and parahippocampa gyrus was the region that identified SCI from MCI, with an AUC of 0.926. CONCLUSION The parahippocampal gyrus was the potential brain region for recognizing cognitive impairment in PD, specifically for identifying MCI. Thus, the fALFF of parahippocampal gyrus is expected to contribute to future study as a multimodal fingerprint for early warning.
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Affiliation(s)
- Peng Chen
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Guoqiang Tang
- Pre‐hospital Emergency Department, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Yanglingxi Wang
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Weiming Xiong
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Yongbing Deng
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - She Fei
- Department of EmergencyThe Fourth Medical Center of the Chinese PLA General HospitalBeijingChina
| | - Jun Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina
- Department of NeurosurgeryClinical Medical College of Yangzhou UniversityYangzhouChina
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Ay U, Yıldırım Z, Erdogdu E, Kiçik A, Ozturk-Isik E, Demiralp T, Gurvit H. Shrinkage of olfactory amygdala connotes cognitive impairment in patients with Parkinson's disease. Cogn Neurodyn 2023; 17:1309-1320. [PMID: 37786655 PMCID: PMC10542039 DOI: 10.1007/s11571-022-09887-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 09/04/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
During the caudo-rostral progression of Lewy pathology, the amygdala is involved relatively early in Parkinson's disease (PD). However, lesser is known about the volumetric differences at the amygdala subdivisions, although the evidence mainly implicates the olfactory amygdala. We aimed to investigate the volumetric differences between the amygdala's nuclear and sectoral subdivisions in the PD cognitive impairment continuum compared to healthy controls (HC). The volumes of nine nuclei of the amygdala were estimated with FreeSurfer (nuclear parcellation-NP) from T1-weighted images of PD patients with normal cognition (PD-CN), PD with mild cognitive impairment (PD-MCI), PD with dementia (PD-D), and HC. The appropriate nuclei were then merged to obtain three sectors of the amygdala (sectoral parcellation-SP). The nuclear and sectoral volumes were compared among the four groups and between the hyposmic and normosmic PD patients. There was a significant difference in the total amygdala volume among the four groups. In terms of nuclei, the bilateral cortico-amygdaloid transition area (CAT) and sectors superficial cortex-like region (sCLR) volumes of PD-MCI and PD-D were less than those of the PD-CN and HC. A linear discriminant analysis revealed that left CAT and left sCLR volumes classified the PD-CN and cognitively impaired PD (PD-CI: PD-MCI plus PD-D) with 90.7% accuracy according to NP and 85.2% accuracy to SP. Similarly, left CAT and sCLR volumes correctly identified the hyposmic and normosmic PD with 64.8% and 61.1% accuracies. Notably, the left olfactory amygdala volume successfully discriminated cognitive impairment in PD and could be used as neuroimaging-based support for PD-CI diagnosis. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09887-y.
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Affiliation(s)
- Ulaş Ay
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Laboratory, Istanbul University, 34093 Istanbul, Turkey
- Graduate School of Health Sciences, Istanbul University, 34126 Istanbul, Turkey
| | - Zerrin Yıldırım
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Laboratory, Istanbul University, 34093 Istanbul, Turkey
- Department of Neurology, Bagcilar Education and Research Hospital, 34200 Istanbul, Turkey
| | - Emel Erdogdu
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Laboratory, Istanbul University, 34093 Istanbul, Turkey
- Department of Psychology, Faculty of Arts and Sciences, Isik University, 34980 Istanbul, Turkey
| | - Ani Kiçik
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Laboratory, Istanbul University, 34093 Istanbul, Turkey
- Department of Physiology, Faculty of Medicine, Demiroglu Bilim University, 34394 Istanbul, Turkey
| | - Esin Ozturk-Isik
- Institute of Biomedical Engineering, Bogazici University, 34684 Istanbul, Turkey
| | - Tamer Demiralp
- Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey
| | - Hakan Gurvit
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey
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Invitto S, Boscolo-Rizzo P, Fantin F, Bonifati DM, de Filippis C, Emanuelli E, Frezza D, Giopato F, Caggiula M, Schito A, Ciccarese V, Spinato G. Exploratory Study on Chemosensory Event-Related Potentials in Long COVID-19 and Mild Cognitive Impairment: A Common Pathway? Bioengineering (Basel) 2023; 10:bioengineering10030376. [PMID: 36978767 PMCID: PMC10045951 DOI: 10.3390/bioengineering10030376] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/13/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
People affected by the Long COVID-19 (LC) syndrome often show clinical manifestations that are similar to those observed in patients with mild cognitive impairments (MCI), such as olfactory dysfunction (OD), brain fog, and cognitive and attentional diseases. This study aimed to investigate the chemosensory-evoked related potentials (CSERP) in LC and MCI to understand if there is a common pathway for the similarity of symptoms associated with these disorders. Eighteen LC patients (mean age 53; s.d. = 7), 12 patients diagnosed with MCI (mean age 67; s.d. = 6), and 10 healthy control subjects (mean age 66; s.d. = 5, 7) were recruited for this exploratory study. All of them performed a chemosensory event-related potentials (CSERP) task with the administration of trigeminal stimulations (e.g., the odorants cinnamaldehyde and eucalyptus). Study results highlighted that MCI and LC showed reduced N1 amplitude, particularly in the left frontoparietal network, involved in working memory and attentional deficits, and a reduction of P3 latency in LC. This study lays the foundations for evaluating aspects of LC as a process that could trigger long-term functional alterations, and CSERPs could be considered valid biomarkers for assessing the progress of OD and an indicator of other impairments (e.g., attentional and cognitive impairments), as they occur in MCI.
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Affiliation(s)
- Sara Invitto
- INSPIRE Lab, Laboratory on Cognitive and Psychophysiological Olfactory Processing, DiSTeBA, University of Salento, 73100 Lecce, Italy
| | - Paolo Boscolo-Rizzo
- Department of Medical, Surgical and Health Sciences, Section of Otolaryngology, University of Trieste, 34123 Trieste, Italy
| | - Francesco Fantin
- Department of Neuroscience DNS, Audiology Unit at Treviso Hospital, University of Padova, 31100 Treviso, Italy
| | - Domenico Marco Bonifati
- Unit of Neurology, Department of Neuro-Cardio-Vascular, Ca' Foncello Hospital, 31100 Treviso, Italy
| | - Cosimo de Filippis
- Department of Neuroscience DNS, University of Padova, Audiology and Phoniatrics Unit, Ca' Foncello Hospital, 31100 Treviso, Italy
| | - Enzo Emanuelli
- Otolaringology Unit, Ca' Foncello Hospital, Local Health Unit N.2 "Marca Trevigiana", 31100 Treviso, Italy
| | - Daniele Frezza
- Otolaringology Unit, Ca' Foncello Hospital, Local Health Unit N.2 "Marca Trevigiana", 31100 Treviso, Italy
| | - Federico Giopato
- Unit of Neurology, Department of Neuro-Cardio-Vascular, Ca' Foncello Hospital, 31100 Treviso, Italy
| | | | - Andrea Schito
- INSPIRE Lab, Laboratory on Cognitive and Psychophysiological Olfactory Processing, DiSTeBA, University of Salento, 73100 Lecce, Italy
- Istituto Santa Chiara, 73100 Lecce, Italy
| | | | - Giacomo Spinato
- Department of Neuroscience DNS, Section of Otorhinolaryngology, University of Padova, 35121 Padova, Italy
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Altered static and dynamic functional network connectivity in post-stroke cognitive impairment. Neurosci Lett 2023; 799:137097. [PMID: 36716911 DOI: 10.1016/j.neulet.2023.137097] [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: 11/20/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 01/29/2023]
Abstract
Post-stroke cognitive impairment (PSCI) is a common symptom following brain stroke, yet the mechanisms remain unknown. This study aimed to investigate alterations of static and dynamic functional network connectivity (sFNC and dFNC) in PSCI patients. We prospectively recruited 17 PSCI patients and 24 Healthy controls (HC). Restingstate fMRI (rs-fMRI) and Mini-Mental State Examination (MMSE) were performed. Independent component analysis combined with sliding-window and K-means clustering approach were applied to examine the FNC among 11 resting-state networks: auditory network (AUDN), left executive control network (lECN), language network (LN), precuneus network (PCUN), right executive control network (rECN), salience network (SN), visuospatial network (VN), dorsal default mode network (dDMN), higher visual network (hVIS), primary visual network (pVIS), and ventral mode network (vDMN). The FNC and dynamic indices (fraction time, mean dwell time, transition number) were calculated. Static and dynamic measures were compared between two groups and the correlation between clinical and imaging indicators was analyzed. For sFNC, PSCI group showed decreased interactions in dDMN-vDMN, vDMN-SN, dDMN-hVIS, AUDN-rECN, and AUDN-VN. For dFNC, we derived 3 states of FNC that occurred repeatedly. Significant group differences were found, including decreased interactions in the AUDN-VN, AUDN-pVIS in state 2 and dDMN-VN in state 3. The mean dwell time in PSCI group was longer in state 1, and negatively correlated with MMSE score. These results demonstrated that PSCI patients are characterized with altered sFNC and dFNC, which could help us explore the neural mechanisms of the PSCI from a new perspective.
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Cengiz S, Arslan DB, Kicik A, Erdogdu E, Yildirim M, Hatay GH, Tufekcioglu Z, Uluğ AM, Bilgic B, Hanagasi H, Demiralp T, Gurvit H, Ozturk-Isik E. Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning. MAGMA (NEW YORK, N.Y.) 2022; 35:997-1008. [PMID: 35867235 DOI: 10.1007/s10334-022-01030-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To investigate metabolic changes of mild cognitive impairment in Parkinson's disease (PD-MCI) using proton magnetic resonance spectroscopic imaging (1H-MRSI). METHODS Sixteen healthy controls (HC), 26 cognitively normal Parkinson's disease (PD-CN) patients, and 34 PD-MCI patients were scanned in this prospective study. Neuropsychological tests were performed, and three-dimensional 1H-MRSI was obtained at 3 T. Metabolic parameters and neuropsychological test scores were compared between PD-MCI, PD-CN, and HC. The correlations between neuropsychological test scores and metabolic intensities were also assessed. Supervised machine learning algorithms were applied to classify HC, PD-CN, and PD-MCI groups based on metabolite levels. RESULTS PD-MCI had a lower corrected total N-acetylaspartate over total creatine ratio (tNAA/tCr) in the right precentral gyrus, corresponding to the sensorimotor network (p = 0.01), and a lower tNAA over myoinositol ratio (tNAA/mI) at a part of the default mode network, corresponding to the retrosplenial cortex (p = 0.04) than PD-CN. The HC and PD-MCI patients were classified with an accuracy of 86.4% (sensitivity = 72.7% and specificity = 81.8%) using bagged trees. CONCLUSION 1H-MRSI revealed metabolic changes in the default mode, ventral attention/salience, and sensorimotor networks of PD-MCI patients, which could be summarized mainly as 'posterior cortical metabolic changes' related with cognitive dysfunction.
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Affiliation(s)
- Sevim Cengiz
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Dilek Betul Arslan
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Ani Kicik
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Center, Istanbul University, Istanbul, Turkey
- Department of Physiology, Faculty of Medicine, Demiroglu Bilim University, Istanbul, Turkey
| | - Emel Erdogdu
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Center, Istanbul University, Istanbul, Turkey
- Department of Psychology, Faculty of Economics and Administrative Sciences, Isik University, Istanbul, Turkey
| | - Muhammed Yildirim
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Gokce Hale Hatay
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Zeynep Tufekcioglu
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
- Department of Neurology, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Aziz Müfit Uluğ
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
- CorTechs Labs, San Diego, CA, USA
| | - Basar Bilgic
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Hasmet Hanagasi
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Tamer Demiralp
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Center, Istanbul University, Istanbul, Turkey
- Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Hakan Gurvit
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Esin Ozturk-Isik
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey.
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Yu RL, Wu RM. Mild cognitive impairment in patients with Parkinson’s disease: An updated mini-review and future outlook. Front Aging Neurosci 2022; 14:943438. [PMID: 36147702 PMCID: PMC9485585 DOI: 10.3389/fnagi.2022.943438] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/15/2022] [Indexed: 12/04/2022] Open
Abstract
Mild cognitive impairment (MCI) is one of the common non-motor symptoms in patients with Parkinson’s disease (PD). MCI is the transition stage between normal aging and full-blown dementia and is also a powerful predictor of dementia. Although the concept of MCI has been used to describe some of the PD symptoms for many years, there is a lack of consistent diagnostic criteria. Moreover, because of the diverse patterns of the cognitive functions, each cognitive impairment will have a different progression. In this review, we overviewed the diagnostic criteria for PD-MCI, primarily focused on the heterogeneity of PD-MCI patients’ cognitive function, including various types of cognitive functions and their progression rates. A review of this topic is expected to be beneficial for clinical diagnosis, early intervention, and treatment. In addition, we also discussed the unmet needs and future vision in this field.
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Affiliation(s)
- Rwei-Ling Yu
- College of Medicine, Institute of Behavioral Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ruey-Meei Wu
- Department of Neurology, College of Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- *Correspondence: Ruey-Meei Wu,
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Wang J, Zhang W, Zhou Y, Jia J, Li Y, Liu K, Ye Z, Jin L. Altered Prefrontal Blood Flow Related With Mild Cognitive Impairment in Parkinson's Disease: A Longitudinal Study. Front Aging Neurosci 2022; 14:896191. [PMID: 35898326 PMCID: PMC9309429 DOI: 10.3389/fnagi.2022.896191] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Cognitive impairment is a common non-motor symptom in Parkinson's disease (PD), with executive dysfunction being an initial manifestation. We aimed to investigate whether and how longitudinal changes in the prefrontal perfusion correlate with mild cognitive impairment (MCI) in patients with PD. We recruited 49 patients with PD with normal cognition and 37 matched healthy control subjects (HCs). Patients with PD completed arterial spin labeling MRI (ASL–MRI) scans and a comprehensive battery of neuropsychological assessments at baseline (V0) and 2-year follow-up (V1). HCs completed similar ASL–MRI scans and neuropsychological assessments at baseline. At V1, 10 patients with PD progressed to MCI (converters) and 39 patients remained cognitively normal (non-converters). We examined differences in the cerebral blood flow (CBF) derived from ASL–MRI and neuropsychological measures (a) between patients with PD and HCs at V0 (effect of the disease), (b) between V1 and V0 in patients with PD (effect of the disease progression), and (c) between converters and non-converters (effect of the MCI progression) using t-tests or ANOVAs with false discovery rate correction. We further analyzed the relationship between longitudinal CBF and neuropsychological changes using multivariate regression models with false discovery rate correction, focusing on executive functions. At V0, no group difference was found in prefrontal CBF between patients with PD and HCs, although patients with PD showed worse performances on executive function. At V1, patients with PD showed significantly reduced CBF in multiple prefrontal regions, including the bilateral lateral orbitofrontal, medial orbitofrontal, middle frontal, inferior frontal, superior frontal, caudal anterior cingulate, and rostral anterior cingulate. More importantly, converters showed a more significant CBF reduction in the left lateral orbitofrontal cortex than non-converters. From V0 to V1, the prolonged completion time of Trail Making Test-B (TMT-B) negatively correlated with longitudinal CBF reduction in the right caudal anterior cingulate cortex. The decreased accuracy of the Stroop Color-Word Test positively correlated with longitudinal CBF reduction in the left medial orbitofrontal cortex. In addition, at V1, the completion time of TMT-B negatively correlated with CBF in the left caudal anterior cingulate cortex. Our findings suggest that longitudinal CBF reduction in the prefrontal cortex might impact cognitive functions (especially executive functions) at the early stages of PD.
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Affiliation(s)
- Jian Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ying Zhou
- Department of Neurology, XiaMen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | - Jia Jia
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuanfang Li
- Department of Neurology, XiaMen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | - Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zheng Ye
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- *Correspondence: Zheng Ye
| | - Lirong Jin
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Lirong Jin
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