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Skaramagkas V, Pentari A, Fotiadis DI, Tsiknakis M. Using the recurrence plots as indicators for the recognition of Parkinson's disease through phonemes assessment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082739 DOI: 10.1109/embc40787.2023.10340177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Parkinson's disease (PD) is considered to be the second most common neurodegenerative disease which affects the patients' life throughout the years. As a consequence, its early diagnosis is of major importance for the improvement of life quality, implying that the severe symptoms can be delayed through appropriate clinical intervention and treatment. Among the most important premature symptoms of PD are the voice impairments of articulation, phonation and prosody. The objective of this study is to investigate whether the voice's dynamic behavior can be used as possible indicator for PD. Thus in this work, we employ the recurrence plots (RPs) which derive from the analysis of the three modulated vowels /a/, /e/ and /o/, which belong to the PC-GITA dataset, and are fed as input images to a 3-channel Convolutional Neural Network-based (CNN) architecture, which, finally, differentiates the 50 PD patients from 50 healthy subjects. The experimental results obtained provide evidence that the RP-based approach is a promising tool for the recognition of PD patients through the analysis of voice recordings, with a classification accuracy achieved equal to 87%.
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Pentari A, Simos N, Tzagarakis G, Kagialis A, Bertsias G, Kavroulakis E, Gratsia E, Sidiropoulos P, Boumpas DT, Papadaki E. Altered hippocampal connectivity dynamics predicts memory performance in neuropsychiatric lupus: a resting-state fMRI study using cross-recurrence quantification analysis. Lupus Sci Med 2023; 10:e000920. [PMID: 37400223 DOI: 10.1136/lupus-2023-000920] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023]
Abstract
OBJECTIVE Τo determine whole-brain and regional functional connectivity (FC) characteristics of patients with neuropsychiatric SLE (NPSLE) or without neuropsychiatric manifestations (non-NPSLE) and examine their association with cognitive performance. METHODS Cross-recurrence quantification analysis (CRQA) of resting-state functional MRI (rs-fMRI) data was performed in 44 patients with NPSLE, 20 patients without NPSLE and 35 healthy controls (HCs). Volumetric analysis of total brain and specific cortical and subcortical regions, where significant connectivity changes were identified, was performed. Cognitive status of patients with NPSLE was assessed by neuropsychological tests. Group comparisons on nodal FC, global network metrics and regional volumetrics were conducted, and associations with cognitive performance were estimated (at p<0.05 false discovery rate corrected). RESULTS FC in patients with NPSLE was characterised by increased modularity (mean (SD)=0.31 (0.06)) as compared with HCs (mean (SD)=0.27 (0.06); p=0.05), hypoconnectivity of the left (mean (SD)=0.06 (0.018)) and right hippocampi (mean (SD)=0.051 (0.0.16)), and of the right amygdala (mean (SD)=0.091 (0.039)), as compared with HCs (mean (SD)=0.075 (0.022), p=0.02; 0.065 (0.019), p=0.01; 0.14 (0.096), p=0.05, respectively). Hyperconnectivity of the left angular gyrus (NPSLE/HCs: mean (SD)=0.29 (0.26) and 0.10 (0.09); p=0.01), left (NPSLE/HCs: mean (SD)=0.16 (0.09) and 0.09 (0.05); p=0.01) and right superior parietal lobule (SPL) (NPSLE/HCs: mean (SD)=0.25 (0.19) and 0.13 (0.13), p=0.01) was noted in NPSLE versus HC groups. Among patients with NPSLE, verbal episodic memory scores were positively associated with connectivity (local efficiency) of the left hippocampus (r2=0.22, p=0.005) and negatively with local efficiency of the left angular gyrus (r2=0.24, p=0.003). Patients without NPSLE displayed hypoconnectivity of the right hippocampus (mean (SD)=0.056 (0.014)) and hyperconnectivity of the left angular gyrus (mean (SD)=0.25 (0.13)) and SPL (mean (SD)=0.17 (0.12)). CONCLUSION By using dynamic CRQA of the rs-fMRI data, distorted FC was found globally, as well as in medial temporal and parietal brain regions in patients with SLE, that correlated significantly and adversely with memory capacity in NPSLE. These results highlight the value of dynamic approaches to assessing impaired brain network function in patients with lupus with and without neuropsychiatric symptoms.
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Affiliation(s)
- Anastasia Pentari
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - Nicholas Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - George Tzagarakis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - Antonios Kagialis
- Department of Psychiatry, University of Crete School of Medicine, Heraklion, Greece
- Department of Radiology, University of Crete School of Medicine, Heraklion, Greece
| | - George Bertsias
- Laboratory of Autoimmunity and Inflammation, Institute of Molecular Biology and Biotechnology, Heraklion, Greece
- Department of Rheumatology, Clinical Immunology and Allergy, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
| | | | - Eirini Gratsia
- Department of Radiology, University of Crete School of Medicine, Heraklion, Greece
| | - Prodromos Sidiropoulos
- Department of Rheumatology, Clinical Immunology and Allergy, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
| | - Dimitrios T Boumpas
- Department of Rheumatology, Clinical Immunology and Allergy, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
- Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Efrosini Papadaki
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
- Department of Radiology, University of Crete School of Medicine, Heraklion, Greece
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Kang Y, Zhang Y, Huang K, Wang Z. Recurrence quantification analysis of periodic dynamics in the default mode network in first-episode drug-naïve schizophrenia. Psychiatry Res Neuroimaging 2023; 329:111583. [PMID: 36577311 DOI: 10.1016/j.pscychresns.2022.111583] [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: 09/21/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Abnormal functional connectivity (FC) within the default model network (DMN) in schizophrenia has been frequently reported in previous studies. However, traditional FC analysis was mostly linear correlations based, with the information on nonlinear or temporally lagged brain signals largely overlooked. Fifty-five first-episode drug-naïve schizophrenia (FES) patients and 53 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scanning. The DMN was extracted using independent component analysis. Recurrence quantification analysis was used to measure the duration, predictability, and complexity of the periodic processes of the nonlinear DMN time series. The Mann‒Whitney U test was conducted to compare these features between FES patients and HCs. The support vector machine was applied to discriminate FES from HCs based on these features. Determinism, which means predictability of periodic process activity, between the ventromedial prefrontal cortex (vMPFC) and posterior cingulate and between the vMPFC and precuneus, was significantly decreased in FES compared with HCs. Determinism between the vMPFC and precuneus was positively correlated with category fluency scores in FES. The classifier achieved 77% accuracy. Our results suggest that synchronized periodicity among DMN brain regions is dysregulated in FES, and the periodicity in BOLD signals may be a promising indicator of brain functional connectivity.
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Affiliation(s)
- Yafei Kang
- Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, School of Psychology, Shaanxi Normal University, Xi'an, China
| | - Youming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Kexin Huang
- West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Zhenhong Wang
- Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, School of Psychology, Shaanxi Normal University, Xi'an, China.
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Simos NJ, Manolitsi K, Luppi AI, Kagialis A, Antonakakis M, Zervakis M, Antypa D, Kavroulakis E, Maris TG, Vakis A, Stamatakis EA, Papadaki E. Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion. Neuroinformatics 2022; 21:427-442. [PMID: 36456762 PMCID: PMC10085953 DOI: 10.1007/s12021-022-09615-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/25/2022] [Accepted: 11/13/2022] [Indexed: 12/04/2022]
Abstract
AbstractTraumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.
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Affiliation(s)
- Nicholas J. Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
| | - Katina Manolitsi
- Department of Neurosurgery, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
- Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Andrea I. Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
| | - Antonios Kagialis
- Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Marios Antonakakis
- Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
| | - Michalis Zervakis
- Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
| | - Despina Antypa
- Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Eleftherios Kavroulakis
- Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Thomas G. Maris
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
- Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Antonios Vakis
- Department of Neurosurgery, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Emmanuel A. Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
| | - Efrosini Papadaki
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
- Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
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