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Xin X, Feng Y, Zang Y, Lou Y, Yao K, Gao X. Multivariate Classification of Brain Blood-Oxygen Signal Complexity for the Diagnosis of Children with Tourette Syndrome. Mol Neurobiol 2022; 59:1249-1261. [PMID: 34981418 DOI: 10.1007/s12035-021-02707-0] [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: 10/08/2021] [Accepted: 12/17/2021] [Indexed: 10/19/2022]
Abstract
Tourette syndrome (TS) is a childhood-onset neuropsychiatric disorder characterized by the presence of multiple motor and vocal tics. Because of its varied clinical expressions and lack of reliable diagnostic biomarker, present TS diagnosis still depends on qualitative descriptions of symptoms. Our study aimed to investigate whether the complexity of resting state brain activity can serve as a potential biomarker for TS diagnosis, since it has been used successfully in various neuropsychiatric disorders, including two common TS comorbidities: attention-deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). In the current study, we used both univariate analysis and multivariate searchlight analysis with both linear and non-linear classification methods to explore the group differences in the complexity of resting state brain blood oxygen level-dependent (BOLD) signals between 25 TS boys without comorbidity and 25 sex, age and educational years matched healthy controls (HCs). We also investigated the relation between symptom severity in TS patients (YGTSS scores) and complexity indices derived from different analysis methods. We found: i) univariate analysis revealed reduced complexity in TS patients in the left cerebellum, left superior frontal gyrus, and left medial frontal gyrus; ii) multivariate analysis with non-linear classification method achieved the highest performance (accuracy: 0.94, sensitivity: 0.96, specificity: 0.92, AUC: 0.95) in bilateral supplementary motor areas; iii) significant correlations were found between complexity index derived from multivariate analysis with non-linear classification method and Tic severity (YGTSS scores) in the left cerebellum (r = 0.523, with YGTSS phonic) and in the right supplementary motor area (r = 0.767, with YGTSS motor). Taken together, these results suggested that complexity of resting state BOLD activity is a highly effective index for differentiating TS patients from normal controls. It has a good potential to be a quantitative biomarker for TS diagnosis.
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Affiliation(s)
- Xiaoyang Xin
- Center for Psychological Sciences, Zhejiang University, Hangzhou, 310027, China
| | - Yixuan Feng
- Eye Center of the 2Nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.,Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou, 310009, China
| | - Yufeng Zang
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, 210015, China
| | - Yuting Lou
- Department of Pediatrics, School of Medicine, the Second Affiliated Hospital, Zhejiang University, Hangzhou, 310058, China
| | - Ke Yao
- Eye Center of the 2Nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China. .,Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou, 310009, China.
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou, 310027, China.
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Puche Sarmiento AC, Bocanegra García Y, Ochoa Gómez JF. Active information storage in Parkinson's disease: a resting state fMRI study over the sensorimotor cortex. Brain Imaging Behav 2019; 14:1143-1153. [PMID: 30684153 DOI: 10.1007/s11682-019-00037-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Parkinson's disease (PD), the second most frequent neurodegenerative disease, affects significantly life quality by a combination of motor and cognitive disturbances. Although it is traditionally associated with basal ganglia dysfunction, cortical alterations are also involved in disease symptoms. Our objective is to evaluate the alterations in brain dynamics in de novo and recently treated PD subjects using a nonlinear method known as Active Information Storage. In the current research, Active Information Storage (AIS) was used to study the complex dynamics in motor cortex spontaneous activity captured using resting state functional Magnetic Resonance Imaging (rs-fMRI) at early-stage in non-medicated and recently medicated PD subjects. Supplementary to AIS, the fractional Amplitude of Low Frequency Fluctuation (fALFF), which is a better-established technique of analysis of rs-fMRI signals, was also evaluated. Compared to healthy subjects, the AIS values were significantly reduced in PD patients over the analyzed motor cortex regions; differences were also found at less extent using the fALFF measure. Correlations between AIS and fALFF values showed that the measures seem to capture similar neuronal phenomena in rs-fMRI data. The highest sensitivity when detecting group differences revealed by AIS, and not captured by traditional linear approaches, suggests that this measure is a promising tool for the analysis of rs-fMRI neural data in PD.
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Affiliation(s)
- Aura Cristina Puche Sarmiento
- Grupo de Investigación en Bioinstrumentación e Ingeniería Clínica, Facultad de Ingeniería, Universidad de Antioquia UdeA, Calle 70 No 52-11, 050010, Medellín, Colombia.
| | - Yamile Bocanegra García
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia UdeA, Calle 70 No 52-11, Medellín, Colombia.,Grupo Neuropsicología y Conducta, Facultad de Medicina, Universidad de Antioquia UdeA, Calle 70 No 52-11, Medellín, Colombia
| | - John Fredy Ochoa Gómez
- Grupo de Investigación en Bioinstrumentación e Ingeniería Clínica, Facultad de Ingeniería, Universidad de Antioquia UdeA, Calle 70 No 52-11, 050010, Medellín, Colombia
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Scheel N, Franke E, Münte TF, Madany Mamlouk A. Dimensional Complexity of the Resting Brain in Healthy Aging, Using a Normalized MPSE. Front Hum Neurosci 2018; 12:451. [PMID: 30510506 PMCID: PMC6252312 DOI: 10.3389/fnhum.2018.00451] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 10/23/2018] [Indexed: 11/28/2022] Open
Abstract
Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.
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Affiliation(s)
- Norman Scheel
- Institute for Neuro- and Bioinformatics, Universität zu Lübeck, Lübeck, Germany.,Department of Neurology, Universität zu Lübeck, Lübeck, Germany.,Department of Radiology, Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, United States
| | - Eric Franke
- Institute for Neuro- and Bioinformatics, Universität zu Lübeck, Lübeck, Germany
| | - Thomas F Münte
- Department of Neurology, Universität zu Lübeck, Lübeck, Germany
| | - Amir Madany Mamlouk
- Institute for Neuro- and Bioinformatics, Universität zu Lübeck, Lübeck, Germany
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Sokunbi MO. Using real-time fMRI brain-computer interfacing to treat eating disorders. J Neurol Sci 2018; 388:109-114. [DOI: 10.1016/j.jns.2018.03.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 03/03/2018] [Accepted: 03/05/2018] [Indexed: 12/12/2022]
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