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Coyle-Asbil HJ, Habegger J, Oliver M, Vallis LA. Enabling the ActiGraph GT9X Link's Idle Sleep Mode and Inertial Measurement Unit Settings Directly Impacts Data Acquisition. Sensors (Basel) 2023; 23:5558. [PMID: 37420725 DOI: 10.3390/s23125558] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 07/09/2023]
Abstract
The ActiGraph GT9X has been implemented in clinical trials to track physical activity and sleep. Given recent incidental findings from our laboratory, the overall aim of this study was to notify academic and clinical researchers of the idle sleep mode (ISM) and inertial measurement unit (IMU)'s interaction, as well as their subsequent effect on data acquisition. Investigations were undertaken using a hexapod robot to test the X, Y and Z sensing axes of the accelerometers. Seven GT9X were tested at frequencies ranging from 0.5 to 2 Hz. Testing was performed for three sets of setting parameters: Setting Parameter 1 (ISMONIMUON), Setting Parameter 2 (ISMOFFIMUON), Setting Parameter 3 (ISMONIMUOFF). The minimum, maximum and range of outputs were compared between the settings and frequencies. Findings indicated that Setting Parameters 1 and 2 were not significantly different, but both were significantly different from Setting Parameter 3. Upon inspection, it was discovered that the ISM was only active during Setting Parameter 3 testing, despite it being enabled in Setting Parameter 1. Researchers should be aware of this when conducting future research using the GT9X.
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Affiliation(s)
- Hannah J Coyle-Asbil
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Janik Habegger
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Michele Oliver
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Lori Ann Vallis
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
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Tong Y, Hocke LM, Frederick BB. Low Frequency Systemic Hemodynamic "Noise" in Resting State BOLD fMRI: Characteristics, Causes, Implications, Mitigation Strategies, and Applications. Front Neurosci 2019; 13:787. [PMID: 31474815 PMCID: PMC6702789 DOI: 10.3389/fnins.2019.00787] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 07/15/2019] [Indexed: 01/06/2023] Open
Abstract
Advances in functional magnetic resonance imaging (fMRI) acquisition have improved signal to noise to the point where the physiology of the subject is the dominant noise source in resting state fMRI data (rsfMRI). Among these systemic, non-neuronal physiological signals, respiration and to some degree cardiac fluctuations can be removed through modeling, or in the case of newer, faster acquisitions such as simultaneous multislice acquisition, simple spectral filtering. However, significant low frequency physiological oscillation (∼0.01-0.15 Hz) remains in the signal. This is problematic, as it is the precise frequency band occupied by the neuronally modulated hemodynamic responses used to study brain connectivity, precluding its removal by spectral filtering. The source of this signal, and its method of production and propagation in the body, have not been conclusively determined. Here, we summarize the defining characteristics of the systemic low frequency noise signal, and review some current theories about the signal source and the evidence supporting them. The strength and distribution of the systemic LFO signal make characterizing and removing it essential for accurate quantification, especially for resting state connectivity, when no stimulation can be compared with the signal. Widespread correlated non-neuronal signals obscure and distort the more localized patterns of neuronal correlations between interacting brain regions; they may even cause apparent connectivity between regions with no neuronal interaction. Here, we discuss a simple method we have developed to parse the global, moving, blood-borne signal from the stationary, neuronal connectivity signals, substantially reducing the negative correlations that result from global signal regression. Finally, we will discuss some of the uses to which the moving systemic low frequency oscillation can be put if we consider it a "signal" carrying information, rather than simply "noise" complicating the interpretation of resting state connectivity. Properly utilizing this signal may offer insights into subtle hemodynamic alterations that can be used as early indicators of circulatory dysfunction in a number of neuropsychiatric conditions, such as prodromal stroke, moyamoya, and Alzheimer's disease.
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Affiliation(s)
- Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Lia M. Hocke
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Blaise B. Frederick
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
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Mohanty R, Nair VA, Tellapragada N, Williams LM, Kang TJ, Prabhakaran V. Identification of Subclinical Language Deficit Using Machine Learning Classification Based on Poststroke Functional Connectivity Derived from Low Frequency Oscillations. Brain Connect 2019; 9:194-208. [PMID: 30398379 DOI: 10.1089/brain.2018.0597] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Post-stroke neuropsychological evaluation is time-intensive in assessing impairments in subjects without overt clinical deficits. We utilized functional connectivity (FC) from ten-minute non-invasive resting-state functional MRI (rs-fMRI) to identify stroke subjects at risk for subclinical language deficit (SLD) using machine learning. Discriminative ability of FC derived from slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz) and low frequency oscillations (LFO; 0.01-0.1 Hz) was compared. Sixty clinically non-aphasic right-handed subjects were categorized into three subgroups based on stroke status and normalized verbal fluency (NVF) score: 20 ischemic early-stage stroke subjects at higher risk for SLD (LD+; mean VFS=-1.77), 20 ischemic early-stage stroke subjects with at risk for SLD (LD-; mean VFS=-0.05), 20 healthy controls (HC; mean VFS=0.29). T1-weighted and rs-fMRI were acquired within 30 days of stroke onset. Blood-oxygen-level-dependent signal was extracted within the language network. FC was evaluated and used by a multiclass support vector machine to classify test subject into a subgroup which was assessed by nested leave-one-out cross-validation. FC derived from slow-4 (70%) provided the best accuracy relative to LFO (65%) and slow-5 (50%), reasonably higher than random chance (33.33%). Using subgroup-specific accuracy, classification was best realized within slow-4 for LD+ (81.6%) and LD- (78.3%) and slow-4/LFO for HC (80%), i.e., early-stage stroke subjects showed a slow-4 FC dominance whereas HC also indicated the normalized involvement within LFO. While frontal FC differentiated stroke from healthy, occipital FC differentiated between the two stroke subgroups. Thus, stroke subjects at risk for SLD can be identified using rs-fMRI reasonably in an expedited manner.
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Affiliation(s)
- Rosaleena Mohanty
- 1 Department of Radiology, Wisconsin Institute of Medical Research (WIMR), University of Wisconsin-Madison, Madison, Wisconsin.,2 Department of Electrical Engineering, Wisconsin Institute of Medical Research (WIMR), University of Wisconsin-Madison, Madison, Wisconsin
| | - Veena A Nair
- 1 Department of Radiology, Wisconsin Institute of Medical Research (WIMR), University of Wisconsin-Madison, Madison, Wisconsin
| | - Neelima Tellapragada
- 1 Department of Radiology, Wisconsin Institute of Medical Research (WIMR), University of Wisconsin-Madison, Madison, Wisconsin
| | - Leroy M Williams
- 1 Department of Radiology, Wisconsin Institute of Medical Research (WIMR), University of Wisconsin-Madison, Madison, Wisconsin
| | - Theresa J Kang
- 1 Department of Radiology, Wisconsin Institute of Medical Research (WIMR), University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- 1 Department of Radiology, Wisconsin Institute of Medical Research (WIMR), University of Wisconsin-Madison, Madison, Wisconsin.,3 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.,4 Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
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Lin SH, Lai HY, Lo YC, Chou C, Chou YT, Yang SH, Sun I, Chen BW, Wang CF, Liu GT, Jaw FS, Chen SY, Chen YY. Decreased Power but Preserved Bursting Features of Subthalamic Neuronal Signals in Advanced Parkinson's Patients under Controlled Desflurane Inhalation Anesthesia. Front Neurosci 2017; 11:701. [PMID: 29311782 PMCID: PMC5733027 DOI: 10.3389/fnins.2017.00701] [Citation(s) in RCA: 9] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 11/28/2017] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) surgery of the subthalamic nucleus (STN) under general anesthesia (GA) had been used in Parkinson's disease (PD) patients who are unable tolerate awake surgery. The effect of anesthetics on intraoperative microelectrode recording (MER) remains unclear. Understanding the effect of anesthetics on MER is important in performing STN DBS surgery with general anesthesia. In this study, we retrospectively performed qualitive and quantitative analysis of STN MER in PD patients received STN DBS with controlled desflurane anesthesia or LA and compared their clinical outcome. From January 2005 to March 2006, 19 consecutive PD patients received bilateral STN DBS surgery in Hualien Tzu-Chi hospital under either desflurane GA (n = 10) or LA (n = 9). We used spike analysis (frequency and modified burst index [MBI]) and the Hilbert transform to obtain signal power measurements for background and spikes, and compared the characterizations of intraoperative microelectrode signals between the two groups. Additionally, STN firing pattern characteristics were determined using a combined approach based on the autocorrelogram and power spectral analysis, which was employed to investigate differences in the oscillatory activities between the groups. Clinical outcomes were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) before and after surgery. The results revealed burst firing was observed in both groups. The firing frequencies were greater in the LA group and MBI was comparable in both groups. Both the background and spikes were of significantly greater power in the LA group. The power spectra of the autocorrelograms were significantly higher in the GA group between 4 and 8 Hz. Clinical outcomes based on the UPDRS were comparable in both groups before and after DBS surgery. Under controlled light desflurane GA, burst features of the neuronal firing patterns are preserved in the STN, but power is reduced. Enhanced low-frequency (4–8 Hz) oscillations in the MERs for the GA group could be a characteristic signature of desflurane's effect on neurons in the STN.
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Affiliation(s)
- Sheng-Huang Lin
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.,Department of Neurology, Tzu Chi General Hospital, Tzu Chi University, Hualien, Taiwan
| | - Hsin-Yi Lai
- Interdisciplinary Institute of Neuroscience and Technology, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Yu-Chun Lo
- The Ph.D. Program for Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Chin Chou
- Department of Biomedical Engineering, National Yang Ming University, Taipei, Taiwan
| | - Yi-Ting Chou
- Department of Biomedical Engineering, National Yang Ming University, Taipei, Taiwan
| | - Shih-Hung Yang
- Department of Mechanical and Computer Aided Engineering, Feng Chia University, Taichung, Taiwan
| | - I Sun
- Department of Life Sciences, Institute of Genome Sciences, National Yang Ming University, Taipei, China
| | - Bo-Wei Chen
- Department of Biomedical Engineering, National Yang Ming University, Taipei, Taiwan
| | - Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming University, Taipei, Taiwan
| | - Guan-Tze Liu
- Department of Medicine, National Yang Ming University, Taipei, Taiwan
| | - Fu-Shan Jaw
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Shin-Yuan Chen
- Department of Neurosurgery, Tzu Chi General Hospital, Tzu Chi University, Hualien, Taiwan
| | - You-Yin Chen
- The Ph.D. Program for Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Department of Biomedical Engineering, National Yang Ming University, Taipei, Taiwan
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Tong Y, Hocke LM, Lindsey KP, Erdoğan SB, Vitaliano G, Caine CE, Frederick BD. Systemic Low-Frequency Oscillations in BOLD Signal Vary with Tissue Type. Front Neurosci 2016; 10:313. [PMID: 27445680 PMCID: PMC4928460 DOI: 10.3389/fnins.2016.00313] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 06/21/2016] [Indexed: 11/21/2022] Open
Abstract
Blood-oxygen-level dependent (BOLD) signals are widely used in functional magnetic resonance imaging (fMRI) as a proxy measure of brain activation. However, because these signals are blood-related, they are also influenced by other physiological processes. This is especially true in resting state fMRI, during which no experimental stimulation occurs. Previous studies have found that the amplitude of resting state BOLD is closely related to regional vascular density. In this study, we investigated how some of the temporal fluctuations of the BOLD signal also possibly relate to regional vascular density. We began by identifying the blood-bound systemic low-frequency oscillation (sLFO). We then assessed the distribution of all voxels based on their correlations with this sLFO. We found that sLFO signals are widely present in resting state BOLD signals and that the proportion of these sLFOs in each voxel correlates with different tissue types, which vary significantly in underlying vascular density. These results deepen our understanding of the BOLD signal and suggest new imaging biomarkers based on fMRI data, such as amplitude of low-frequency fluctuation (ALFF) and sLFO, a combination of both, for assessing vascular density.
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Affiliation(s)
- Yunjie Tong
- McLean Imaging Center, McLean HospitalBelmont, MA, USA; Department of Psychiatry, Harvard University Medical SchoolBoston, MA, USA
| | - Lia M Hocke
- McLean Imaging Center, McLean HospitalBelmont, MA, USA; Department of Radiology, University of CalgaryCalgary, AB, Canada
| | - Kimberly P Lindsey
- McLean Imaging Center, McLean HospitalBelmont, MA, USA; Department of Psychiatry, Harvard University Medical SchoolBoston, MA, USA
| | | | - Gordana Vitaliano
- McLean Imaging Center, McLean HospitalBelmont, MA, USA; Department of Psychiatry, Harvard University Medical SchoolBoston, MA, USA
| | | | - Blaise deB Frederick
- McLean Imaging Center, McLean HospitalBelmont, MA, USA; Department of Psychiatry, Harvard University Medical SchoolBoston, MA, USA
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Liu WM, Meyer J, Scully CG, Elster E, Gorbach AM. Observing temperature fluctuations in humans using infrared imaging. Quant Infrared Thermogr J 2011; 8:21-36. [PMID: 23538682 PMCID: PMC3607327 DOI: 10.3166/qirt.8.21-36] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In this work we demonstrate that functional infrared imaging is capable of detecting low frequency temperature fluctuations in intact human skin and revealing spatial, temporal, spectral, and time-frequency based differences among three tissue classes: microvasculature, large sub-cutaneous veins, and the remaining surrounding tissue of the forearm. We found that large veins have stronger contractility in the range of 0.005-0.06 Hz compared to the other two tissue classes. Wavelet phase coherence and power spectrum correlation analysis show that microvasculature and skin areas without vessels visible by IR have high phase coherence in the lowest three frequency ranges (0.005-0.0095 Hz, 0.0095-0.02 Hz, and 0.02-0.06 Hz), whereas large veins oscillate independently.
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Affiliation(s)
- Wei-Min Liu
- Naval Medical Research Center, Silver Spring, MD, USA ; National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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