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Kaposzta Z, Czoch A, Mukli P, Stylianou O, Liu DH, Eke A, Racz FS. Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging. GeroScience 2024; 46:713-736. [PMID: 38117421 PMCID: PMC10828149 DOI: 10.1007/s11357-023-01022-x] [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: 09/25/2023] [Accepted: 11/19/2023] [Indexed: 12/21/2023] Open
Abstract
Analysis of brain functional connectivity (FC) could provide insight in how and why cognitive functions decline even in healthy aging (HA). Despite FC being established as fluctuating over time even in the resting state (RS), dynamic functional connectivity (DFC) studies involving healthy elderly individuals and assessing how these patterns relate to cognitive performance are yet scarce. In our recent study we showed that fractal temporal scaling of functional connections in RS is not only reduced in HA, but also predicts increased response latency and reduced task solving accuracy. However, in that work we did not address changes in the dynamics of fractal connectivity (FrC) strength itself and its plausible relationship with mental capabilities. Therefore, here we analyzed RS electroencephalography recordings of the same subject cohort as previously, consisting of 24 young and 19 healthy elderly individuals, who also completed 7 different cognitive tasks after data collection. Dynamic fractal connectivity (dFrC) analysis was carried out via sliding-window detrended cross-correlation analysis (DCCA). A machine learning method based on recursive feature elimination was employed to select the subset of connections most discriminative between the two age groups, identifying 56 connections that allowed for classifying participants with an accuracy surpassing 92%. Mean of DCCA was found generally increased, while temporal variability of FrC decreased in the elderly when compared to the young group. Finally, dFrC indices expressed an elaborate pattern of associations-assessed via Spearman correlation-with cognitive performance scores in both groups, linking fractal connectivity strength and variance to increased response latency and reduced accuracy in the elderly population. Our results provide further support for the relevance of FrC dynamics in understanding age-related cognitive decline and might help to identify potential targets for future intervention strategies.
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Affiliation(s)
- Zalan Kaposzta
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
| | - Peter Mukli
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Orestis Stylianou
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Deland Hu Liu
- Chandra Department of Electrical and Computer Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Andras Eke
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary.
- Department of Neurology, Dell Medical School, The University of Texas at Austin, 1601 Trinity St, Austin, TX, 78712, USA.
- Mulva Clinic for the Neurosciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
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Varanasi S, Tuli R, Han F, Chen R, Choa FS. Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031603. [PMID: 36772649 PMCID: PMC9920122 DOI: 10.3390/s23031603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 05/14/2023]
Abstract
The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network's connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.
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Affiliation(s)
- Sravani Varanasi
- Department of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA
- Correspondence:
| | - Roopan Tuli
- Department of Electrical Engineering, Santa Clara University, Santa Clara, CA 95053, USA
| | - Fei Han
- The Hilltop Institute, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, Baltimore, MD 21201, USA
| | - Fow-Sen Choa
- Department of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA
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Zhao L, Zeng W, Shi Y, Nie W. Dynamic effective connectivity network based on change points detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Complexity Analysis of the Default Mode Network Using Resting-State fMRI in Down Syndrome: Relationships Highlighted by a Neuropsychological Assessment. Brain Sci 2021; 11:brainsci11030311. [PMID: 33801471 PMCID: PMC8001398 DOI: 10.3390/brainsci11030311] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/21/2021] [Accepted: 02/25/2021] [Indexed: 01/04/2023] Open
Abstract
Background: Studies on complexity indicators in the field of functional connectivity derived from resting-state fMRI (rs-fMRI) in Down syndrome (DS) samples and their possible relationship with cognitive functioning variables are rare. We analyze how some complexity indicators estimated in the subareas that constitute the default mode network (DMN) might be predictors of the neuropsychological outcomes evaluating Intelligence Quotient (IQ) and cognitive performance in persons with DS. Methods: Twenty-two DS people were assessed with the Kaufman Brief Test of Intelligence (KBIT) and Frontal Assessment Battery (FAB) tests, and fMRI signals were recorded in a resting state over a six-minute period. In addition, 22 controls, matched by age and sex, were evaluated with the same rs-fMRI procedure. Results: There was a significant difference in complexity indicators between groups: the control group showed less complexity than the DS group. Moreover, the DS group showed more variance in the complexity indicator distributions than the control group. In the DS group, significant and negative relationships were found between some of the complexity indicators in some of the DMN networks and the cognitive performance scores. Conclusions: The DS group is characterized by more complex DMN networks and exhibits an inverse relationship between complexity and cognitive performance based on the negative parameter estimates.
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