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Sparacino L, Faes L, Mijatović G, Parla G, Lo Re V, Miraglia R, de Ville de Goyet J, Sparacia G. Statistical Approaches to Identify Pairwise and High-Order Brain Functional Connectivity Signatures on a Single-Subject Basis. Life (Basel) 2023; 13:2075. [PMID: 37895456 PMCID: PMC10608185 DOI: 10.3390/life13102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/21/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Keeping up with the shift towards personalized neuroscience essentially requires the derivation of meaningful insights from individual brain signal recordings by analyzing the descriptive indexes of physio-pathological states through statistical methods that prioritize subject-specific differences under varying experimental conditions. Within this framework, the current study presents a methodology for assessing the value of the single-subject fingerprints of brain functional connectivity, assessed both by standard pairwise and novel high-order measures. Functional connectivity networks, which investigate the inter-relationships between pairs of brain regions, have long been a valuable tool for modeling the brain as a complex system. However, their usefulness is limited by their inability to detect high-order dependencies beyond pairwise correlations. In this study, by leveraging multivariate information theory, we confirm recent evidence suggesting that the brain contains a plethora of high-order, synergistic subsystems that would go unnoticed using a pairwise graph structure. The significance and variations across different conditions of functional pairwise and high-order interactions (HOIs) between groups of brain signals are statistically verified on an individual level through the utilization of surrogate and bootstrap data analyses. The approach is illustrated on the single-subject recordings of resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired using a pediatric patient with hepatic encephalopathy associated with a portosystemic shunt and undergoing liver vascular shunt correction. Our results show that (i) the proposed single-subject analysis may have remarkable clinical relevance for subject-specific investigations and treatment planning, and (ii) the possibility of investigating brain connectivity and its post-treatment functional developments at a high-order level may be essential to fully capture the complexity and modalities of the recovery.
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Affiliation(s)
- Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.S.); (L.F.)
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.S.); (L.F.)
| | - Gorana Mijatović
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia;
| | - Giuseppe Parla
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
| | | | - Roberto Miraglia
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
| | - Jean de Ville de Goyet
- Department for the Treatment and Study of Pediatric Abdominal Diseases and Abdominal Transplantation, IRCCS-ISMETT, 90127 Palermo, Italy;
| | - Gianvincenzo Sparacia
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
- Radiology Service, BiND, University of Palermo, 90128 Palermo, Italy
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Lo Re V, Russelli G, Lo Gerfo E, Alduino R, Bulati M, Iannolo G, Terzo D, Martucci G, Anzani S, Panarello G, Sparacia G, Parla G, Avorio F, Raffa G, Pilato M, Speciale A, Agnese V, Mamone G, Tuzzolino F, Vizzini GB, Conaldi PG, Ambrosio F. Cognitive outcomes in patients treated with neuromuscular electrical stimulation after coronary artery bypass grafting. Front Neurol 2023; 14:1209905. [PMID: 37693766 PMCID: PMC10486105 DOI: 10.3389/fneur.2023.1209905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023] Open
Abstract
Objective Mechanisms of neurocognitive injury as post-operative sequelae of coronary artery bypass grafting (CABG) are not understood. The systemic inflammatory response to surgical stress causes skeletal muscle impairment, and this is also worsened by immobility. Since evidence supports a link between muscle vitality and neuroprotection, there is a need to understand the mechanisms by which promotion of muscle activity counteracts the deleterious effects of surgery on long-term cognition. Methods We performed a clinical trial to test the hypothesis that adding neuromuscular electrical stimulation (NMES) to standard rehabilitation care in post-CABG patients promotes the maintenance of skeletal muscle strength and the expression of circulating neuroprotective myokines. Results We did not find higher serum levels of neuroprotective myokines, except for interleukin-6, nor better long-term cognitive performance in our intervention group. However, a greater increase in functional connectivity at brain magnetic resonance was seen between seed regions within the default mode, frontoparietal, salience, and sensorimotor networks in the NMES group. Regardless of the treatment protocol, patients with a Klotho increase 3 months after hospital discharge compared to baseline Klotho values showed better scores in delayed memory tests. Significance We confirm the potential neuroprotective effect of Klotho in a clinical setting and for the first time post-CABG.
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Affiliation(s)
- Vincenzina Lo Re
- Neurology Service, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT (Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione), University of Pittsburgh Medical Center (UPMC), Palermo, Italy
| | | | - Emanuele Lo Gerfo
- Neurology Service, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT (Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione), University of Pittsburgh Medical Center (UPMC), Palermo, Italy
- Department of Research, IRCCS ISMETT, UPMC, Palermo, Italy
| | | | - Matteo Bulati
- Department of Research, IRCCS ISMETT, UPMC, Palermo, Italy
| | | | - Danilo Terzo
- Rehabilitation Service, IRCCS ISMETT, Palermo, Italy
| | - Gennaro Martucci
- Department of Anesthesiology and Intensive Care, IRCCS ISMETT, UPMC, Palermo, Italy
| | - Stefano Anzani
- Neurology Service, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT (Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione), University of Pittsburgh Medical Center (UPMC), Palermo, Italy
- Department of Research, IRCCS ISMETT, UPMC, Palermo, Italy
| | - Giovanna Panarello
- Department of Anesthesiology and Intensive Care, IRCCS ISMETT, UPMC, Palermo, Italy
| | - Gianvincenzo Sparacia
- Radiology Unit, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT, Palermo, Italy
| | - Giuseppe Parla
- Radiology Unit, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT, Palermo, Italy
| | - Federica Avorio
- Neurology Service, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT (Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione), University of Pittsburgh Medical Center (UPMC), Palermo, Italy
| | - Giuseppe Raffa
- Cardiac Surgery Unit, Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS ISMETT, Palermo, Italy
| | - Michele Pilato
- Cardiac Surgery Unit, Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS ISMETT, Palermo, Italy
| | | | | | - Giuseppe Mamone
- Radiology Unit, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT, Palermo, Italy
| | | | | | | | - Fabrisia Ambrosio
- Discovery Center for Musculoskeletal Recovery, Schoen Adams Research Institute at Spaulding, Boston, MA, United States
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, United States
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Sparacino L, Valentino M, Antonacci Y, Parla G, Sparacia G, Faes L. Statistical Approaches to Characterize Functional Connectivity in Brain and Physiologic Networks on a Single-Subject Basis. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38083104 DOI: 10.1109/embc40787.2023.10340969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The trend toward personalized medicine necessitates drawing conclusions from descriptive indexes of physiopathological states estimated from individual recordings of biomedical signals, using statistical analyses that focus on subject-specific differences between experimental conditions. In this context, the present work introduces an approach to assess functional connectivity in brain and physiologic networks by pairwise information-theoretic measures of coupling between signals, whose significance and variations between conditions are statistically validated on a single-subject basis through the use of surrogate and bootstrap data analyses. The approach is illustrated on single-subject recordings of (i) resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired in a pediatric patient with hepatic encephalography associated to a portosystemic shunt and undergoing liver vascular shunt correction, and of (ii) cardiovascular and cerebrovascular time series acquired at rest and during head-up tilt in a subject suffering from orthostatic intolerance.
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Guerrieri M, Parla G. Real-time social distance measurement and face mask detection in public transportation systems during the COVID-19 pandemic and post-pandemic Era: Theoretical approach and case study in Italy. Transp Res Interdiscip Perspect 2022; 16:100693. [PMID: 36187495 PMCID: PMC9515336 DOI: 10.1016/j.trip.2022.100693] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/12/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
Due to its remarkable learning ability and benefits in several areas of real-life, deep learning-based applications have recovered to be a research topic of great importance in the last few years. This article presents a method devoted to guaranteeing safety conditions in public transportation systems (PTS) during the COVID-19 pandemic and post-pandemic era. The paper describes a viable real-time model based on deep learning for monitoring social distance between users and detecting face masks in stop areas and inside vehicles of public transportation systems. Detections are made using the deep learning approach and YOLOv3 algorithm. The safety rule violations are represented by red bounding boxes and red circles in a bird's eye view as output of the video surveillance analysis. The datasets used to train the neural network are the "Caltech Pedestrian Dataset" and the "COVID-19 Medical Face Mask Detection Dataset". Metrics, such Loss Accuracy, and Precision, obtained in the testing process of the neural network were used to evaluate the performance of the model in detecting users and face masks. The proposed method was recently tested in the Public Transportation System of the Municipality of Piazza Armerina (Italy). The results show a significant reliability of the method in detecting real-time interactions between users of the PTS in terms of over-time variations in their mutual distancing, as well as in recognising cases of violation of the imposed social distancing and FFP2 face mask use.
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Affiliation(s)
- Marco Guerrieri
- DICAM (Department of Civil, Environmental and Mechanical Engineering), University of Trento, Via Mesiano 77, 38123 Trento, Italy
| | - Giuseppe Parla
- ISMET (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), via Tricomi 5 90127, Palermo, Italy
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Sparacia G, Parla G, Lo Re V, Cannella R, Mamone G, Carollo V, Midiri M, Grasso G. Resting-State Functional Connectome in Patients with Brain Tumors Before and After Surgical Resection. World Neurosurg 2020; 141:e182-e194. [PMID: 32428723 DOI: 10.1016/j.wneu.2020.05.054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/05/2020] [Accepted: 05/06/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE High-grade glioma surgery has evolved around the principal belief that a safe maximal tumor resection improves symptoms, quality of life, and survival. Mapping brain function has been recently improved by resting-state functional magnetic resonance imaging (rest-fMRI), a novel imaging technique that explores networks connectivity at "rest." METHODS This prospective study analyzed 10 patients with high-grade glioma in whom rest-fMRI connectivity was assessed both in single-subject and in group analysis before and after surgery. Seed-based functional connectivity analysis was performed with CONN toolbox. Network identification focused on 8 major functional connectivity networks. A voxel-wise region of interest (ROI) to ROI correlation map to assess functional connectivity throughout the whole brain was computed from a priori seeds ROI in specific resting-state networks before and after surgical resection in each patient. RESULTS Reliable topography of all 8 resting-state networks was successfully identified in each participant before surgical resection. Single-subject functional connectivity analysis showed functional disconnection for dorsal attention and salience networks, whereas the language network demonstrated functional connection either in the case of left temporal glioblastoma. Functional connectivity in group analysis showed wide variations of functional connectivity in the default mode, salience, and sensorimotor networks. However, salience and language networks, salience and default mode networks, and salience and sensorimotor networks showed a significant correlation (P uncorrected <0.0025; P false discovery rate <0.077) in comparison before and after surgery confirming non-disconnection of these networks. CONCLUSIONS Resting-state fMRI can reliably detect common functional connectivity networks in patients with glioma and has the potential to anticipate network alterations after surgical resection.
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Affiliation(s)
- Gianvincenzo Sparacia
- Radiology Service, Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy; Radiology Service, Department of Diagnostic and Therapeutic Services, Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS-ISMETT), Palermo, Italy.
| | - Giuseppe Parla
- Radiology Service, Department of Diagnostic and Therapeutic Services, Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS-ISMETT), Palermo, Italy
| | - Vincenzina Lo Re
- Neurology Service, Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS-ISMETT), Palermo, Italy
| | - Roberto Cannella
- Radiology Service, Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Giuseppe Mamone
- Radiology Service, Department of Diagnostic and Therapeutic Services, Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS-ISMETT), Palermo, Italy
| | - Vincenzo Carollo
- Radiology Service, Department of Diagnostic and Therapeutic Services, Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS-ISMETT), Palermo, Italy
| | - Massimo Midiri
- Radiology Service, Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Giovanni Grasso
- Neurosurgical Unit, Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
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