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Di J, Tuttle PG, Adamowicz L, Lin W, Zhang H, Psaltos D, Selig J, Bai J, Karahanoglu FI, Sheriff P, Seelam V, Williams B, Ghafoor S, Demanuele C, Santamaria M, Cai X. Monitoring Activity and Gait in Children (MAGIC) using digital health technologies. Pediatr Res 2024:10.1038/s41390-024-03147-x. [PMID: 38514860 DOI: 10.1038/s41390-024-03147-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/22/2024] [Accepted: 03/02/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Digital health technologies (DHTs) can collect gait and physical activity in adults, but limited studies have validated these in children. This study compared gait and physical activity metrics collected using DHTs to those collected by reference comparators during in-clinic sessions, to collect a normative accelerometry dataset, and to evaluate participants' comfort and their compliance in wearing the DHTs at-home. METHODS The MAGIC (Monitoring Activity and Gait in Children) study was an analytical validation study which enrolled 40, generally healthy participants aged 3-17 years. Gait and physical activity were collected using DHTs in a clinical setting and continuously at-home. RESULTS Overall good to excellent agreement was observed between gait metrics extracted with a gait algorithm from a lumbar-worn DHT compared to ground truth reference systems. Majority of participants either "agreed" or "strongly agreed" that wrist and lumbar DHTs were comfortable to wear at home, respectively, with 86% (wrist-worn DHT) and 68% (lumbar-worn DHT) wear-time compliance. Significant differences across age groups were observed in multiple gait and activity metrics obtained at home. CONCLUSIONS Our findings suggest that gait and physical activity data can be collected from DHTs in pediatric populations with high reliability and wear compliance, in-clinic and in home environments. TRIAL REGISTRATION ClinicalTrials.gov: NCT04823650 IMPACT: Digital health technologies (DHTs) have been used to collect gait and physical activity in adult populations, but limited studies have validated these metrics in children. The MAGIC study comprehensively validates the performance and feasibility of DHT-measured gait and physical activity in the pediatric population. Our findings suggest that reliable gait and physical activity data can be collected from DHTs in pediatric populations, with both high accuracy and wear compliance both in-clinic and in home environments. The identified across-age-group differences in gait and activity measurements highlighted their potential clinical value.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xuemei Cai
- Pfizer, Inc., Cambridge, MA, USA
- Tufts Medical Center, Boston, MA, USA
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Camerlingo N, Shaafi Kabiri N, Psaltos DJ, Kelly M, Wicker MK, Messina I, Auerbach SH, Zhang H, Messere A, Isik Karahanoglu F, Santamaria M, Demanuele C, Caouette D, Thomas KC. Monitoring Gait and Physical Activity of Elderly Frail Individuals in Free-Living Environment: A Feasibility Study. Gerontology 2023; 70:439-454. [PMID: 37984340 PMCID: PMC11014463 DOI: 10.1159/000535283] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 11/11/2023] [Indexed: 11/22/2023] Open
Abstract
INTRODUCTION Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants' frailty status, as measured by conventional assessments. METHODS Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1-2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. RESULTS Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. CONCLUSIONS This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants' travels to sites, will be further assessed in a follow-up study.
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Affiliation(s)
- Nunzio Camerlingo
- Early Clinical Development, Pfizer, Inc., Cambridge, Massachusetts, USA
| | - Nina Shaafi Kabiri
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
| | | | - Meredith Kelly
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
| | - Madisen K Wicker
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
| | - Isabelle Messina
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
| | - Sanford H Auerbach
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
| | - Hao Zhang
- Early Clinical Development, Pfizer, Inc., Cambridge, Massachusetts, USA
| | - Andrew Messere
- Early Clinical Development, Pfizer, Inc., Cambridge, Massachusetts, USA
| | | | - Mar Santamaria
- Early Clinical Development, Pfizer, Inc., Cambridge, Massachusetts, USA
| | | | - David Caouette
- Early Clinical Development, Pfizer, Inc., Cambridge, Massachusetts, USA
| | - Kevin C Thomas
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
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Lin W, Karahanoglu FI, Psaltos D, Adamowicz L, Santamaria M, Cai X, Demanuele C, Di J. Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers? Sensors (Basel) 2023; 23:8542. [PMID: 37896635 PMCID: PMC10611403 DOI: 10.3390/s23208542] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023]
Abstract
Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.
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Affiliation(s)
- Wenyi Lin
- Pfizer Inc., Cambridge, MA 02139, USA (C.D.); (J.D.)
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4
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Rose MJ, Neogi T, Friscia B, Torabian KA, LaValley MP, Gheller M, Adamowicz L, Georgiev P, Viktrup L, Demanuele C, Wacnik PW, Kumar D. Reliability of Wearable Sensors for Assessing Gait and Chair Stand Function at Home in People With Knee Osteoarthritis. Arthritis Care Res (Hoboken) 2023; 75:1939-1948. [PMID: 36734316 PMCID: PMC10397366 DOI: 10.1002/acr.25096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 01/20/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To assess the reliability of wearable sensors for at-home assessment of walking and chair stand activities in people with knee osteoarthritis (OA). METHODS Baseline data from participants with knee OA (n = 20) enrolled in a clinical trial of an exercise intervention were used. Participants completed an in-person laboratory visit and a video conference-enabled at-home visit. In both visits, participants performed walking and chair stand tasks while fitted with 3 inertial sensors. During the at-home visit, participants self-donned the sensors and completed 2 sets of acquisitions separated by a 15-minute break, when they removed and redonned the sensors. Participants completed a survey on their experience with the at-home visit. During the laboratory visit, researchers placed the sensors on the participants. Spatiotemporal metrics of walking gait and chair stand duration were extracted from the sensor data. We used intraclass correlation coefficients (ICCs) and the Bland-Altman plot for statistical analyses. RESULTS For test-retest reliability during the at-home visit, all ICCs were good to excellent (0.85-0.95). For agreement between at-home and laboratory visits, ICCs were moderate to good (0.59-0.87). Systematic differences were noted between at-home and laboratory data due to faster task speed during the laboratory visits. Participants reported a favorable experience during the at-home visit. CONCLUSION Our method of estimating spatiotemporal gait measures and chair stand duration function remotely was reliable, feasible, and acceptable in people with knee OA. Wearable sensors could be used to remotely assess walking and chair stand in participant's natural environments in future studies.
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Affiliation(s)
- Michael J. Rose
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
| | - Tuhina Neogi
- Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Brian Friscia
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
| | - Kaveh A. Torabian
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
| | - Michael P. LaValley
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mary Gheller
- Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | | | | | - Lars Viktrup
- Neuroscience, Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Paul W. Wacnik
- Early Clinical Development, Pfizer Inc., Cambridge, MA, USA
| | - Deepak Kumar
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
- Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
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Izmailova ES, Demanuele C, McCarthy M. Digital health technology derived measures: Biomarkers or clinical outcome assessments? Clin Transl Sci 2023; 16:1113-1120. [PMID: 37118983 PMCID: PMC10339690 DOI: 10.1111/cts.13529] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/22/2023] [Accepted: 03/25/2023] [Indexed: 04/30/2023] Open
Abstract
Digital health technologies (DHTs) present unique opportunities for clinical evidence generation but pose certain challenges. These challenges stem, in part, from existing definitions of drug development tools, which were not created with DHT-derived measures in mind. DHT-derived measures can be leveraged as either clinical outcome assessments (COAs) or as biomarkers since they share properties with both categories of drug development tools. Examples from the literature indicate a variety of applications for DHT-derived data, including capturing disease physiology, symptom tracking, or response to therapies. The distinction between the categorization of DHT-derived measures as COAs or as biomarkers can be very fine, with terminology variability among regulatory authorities. This has significant implications for integration of DHT-derived measures in clinical trials, leading to confusion regarding the evidence required to support these tools' use in drug development. There is a need to amend definitions and create clear evidentiary requirements to support broad adoption of these new and innovative tools. The biopharma industry, the technology sector, consulting businesses, academic researchers, and regulators need a dialogue via multi-stakeholder collaborations to clarify questions around DHT-derived measures, to unify definitions, and to create the foundations for evidentiary package requirements, providing a path forward to predictable results.
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Mc Carthy M, Burrows K, Griffiths P, Black PM, Demanuele C, Karlsson N, Buenconsejo J, Patel N, Chen WH, Cappelleri JC. From Meaningful Outcomes to Meaningful Change Thresholds: A Path to Progress for Establishing Digital Endpoints. Ther Innov Regul Sci 2023; 57:629-645. [PMID: 37020160 DOI: 10.1007/s43441-023-00502-8] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 02/24/2023] [Indexed: 04/07/2023]
Abstract
This paper examines the use of digital endpoints (DEs) derived from digital health technologies (DHTs), focusing primarily on the specific considerations regarding the determination of meaningful change thresholds (MCT). Using DHTs in drug development is becoming more commonplace. There is general acceptance of the value of DHTs supporting patient-centric trial design, capturing data outside the traditional clinical trial setting, and generating DEs with the potential to be more sensitive to change than conventional assessments. However, the transition from exploratory endpoints to primary and secondary endpoints capable of supporting labeling claims requires these endpoints to be substantive with reproducible population-specific values. Meaningful change represents the amount of change in an endpoint measure perceived as important to patients and should be determined for each digital endpoint and given population under consideration. This paper examines existing approaches to determine meaningful change thresholds and explores examples of these methodologies and their use as part of DE development: emphasizing the importance of determining what aspects of health are important to patients and ensuring the DE captures these concepts of interest and aligns with the overarching endpoint strategy. Examples are drawn from published DE qualification documentation and responses to qualification submissions under review by the various regulatory authorities. It is the hope that these insights will inform and strengthen the development and validation of DEs as drug development tools, particularly for those new to the approaches to determine MCTs.
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Ghadessi M, Di J, Wang C, Toyoizumi K, Shao N, Mei C, Demanuele C, Tang RS, McMillan G, Beckman RA. Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective. Orphanet J Rare Dis 2023; 18:79. [PMID: 37041605 PMCID: PMC10088572 DOI: 10.1186/s13023-023-02693-7] [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: 01/06/2023] [Accepted: 04/02/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials. MAIN BODY Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home. CONCLUSION This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.
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Affiliation(s)
- Mercedeh Ghadessi
- Research and Early Development Statistics, Bayer U.S. LLC, 100 Bayer Boulevard, Pharmaceuticals, Whippany, NJ, 07981, USA
| | - Junrui Di
- Global Product Development, Pfizer Inc, Cambridge, MA, 02139, USA.
| | - Chenkun Wang
- Biostatistics department, Vertex Pharmaceuticals, Inc, 50 Northern Avenue, Boston, MA, 02210, USA
| | - Kiichiro Toyoizumi
- Statistics & Decision Sciences Department, Janssen Pharmaceutical K. K, 5-2, Nishi-kanda 3- chome, Chiyoda-ku, Tokyo, 101-0065, Japan
| | - Nan Shao
- Biostatistics, Moderna, Inc, 200 Technology Square, Cambridge, MA, 02139, USA
| | - Chaoqun Mei
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Berkeley Heights, NJ, 07922, USA
| | | | - Rui Sammi Tang
- Clinical Development, Global Biometric Department, Servier pharmaceuticals, 200 Pier Four Blvd, Boston, MA, 02210, USA
| | - Gianna McMillan
- Bioethics Institute at Loyola Marymount University, 1 LMU Drive, Los Angeles, CA, 90045, USA
| | - Robert A Beckman
- Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, 20007, USA
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Psaltos DJ, Mamashli F, Adamusiak T, Demanuele C, Santamaria M, Czech MD. Wearable-Based Stair Climb Power Estimation and Activity Classification. Sensors (Basel) 2022; 22:6600. [PMID: 36081058 PMCID: PMC9459813 DOI: 10.3390/s22176600] [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] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0−6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3−6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.
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Demanuele C, Lokker C, Jhaveri K, Georgiev P, Sezgin E, Geoghegan C, Zou KH, Izmailova E, McCarthy M. Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies. Digit Biomark 2022; 6:47-60. [PMID: 35949223 PMCID: PMC9294934 DOI: 10.1159/000525080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/07/2022] [Indexed: 12/21/2022] Open
Abstract
Background Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own “device” (BYOD) manner where participants use their technologies to generate study data. Summary The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving. Key Messages We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.
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Affiliation(s)
| | | | - Krishna Jhaveri
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania, USA
| | | | - Emre Sezgin
- The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
| | | | - Kelly H. Zou
- Global Medical Analytics and Real-World Evidence, Viatris Inc, Canonsburg, Pennsylvania, USA
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Di J, Demanuele C, Kettermann A, Karahanoglu FI, Cappelleri JC, Potter A, Bury D, Cedarbaum JM, Byrom B. Considerations to address missing data when deriving clinical trial endpoints from digital health technologies. Contemp Clin Trials 2021; 113:106661. [PMID: 34954098 DOI: 10.1016/j.cct.2021.106661] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 08/18/2021] [Revised: 11/23/2021] [Accepted: 12/18/2021] [Indexed: 11/25/2022]
Abstract
Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspective in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.
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Affiliation(s)
- Junrui Di
- Pfizer Inc., United States of America.
| | | | | | | | | | | | | | - Jesse M Cedarbaum
- Yale University School of Medicine, United States of America; Coeruleus Clinical Sciences LLC, United States of America
| | - Bill Byrom
- Signant Health, United States of America
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Adamowicz L, Karahanoglu FI, Cicalo C, Zhang H, Demanuele C, Santamaria M, Cai X, Patel S. Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back. Sensors (Basel) 2020; 20:s20226618. [PMID: 33228035 PMCID: PMC7699326 DOI: 10.3390/s20226618] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 11/10/2020] [Accepted: 11/17/2020] [Indexed: 06/11/2023]
Abstract
The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms for detecting STS transfers rely on multiple sensing modalities or device locations and have predominantly been used for assessment during the performance of prescribed tasks in a lab setting. A novel wavelet-based algorithm for detecting STS transfers from data recorded using an accelerometer on the lower back is presented herein. The proposed algorithm is independent of device orientation and was validated on data captured in the lab from younger and older healthy adults as well as in people with Parkinson's disease (PwPD). The algorithm was then used for processing data captured in free-living conditions to assess the ability of multiple features extracted from STS transfers to detect age-related group differences and assess the impact of monitoring duration on the reliability of measurements. The results show that performance of the proposed algorithm was comparable or significantly better than that of a commercially available system (precision: 0.990 vs. 0.868 in healthy adults) and a previously published algorithm (precision: 0.988 vs. 0.643 in persons with Parkinson's disease). Moreover, features extracted from STS transfers at home were able to detect age-related group differences at a higher level of significance compared to data captured in the lab during the performance of prescribed tasks. Finally, simulation results showed that a monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features. These results point towards the feasibility of using a single accelerometer on the lower back for detection and assessment of STS transfers during daily life. Future work in different patient populations is needed to evaluate the performance of the proposed algorithm, as well as assess the sensitivity and reliability of the STS features.
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Czech M, Demanuele C, Erb MK, Ramos V, Zhang H, Ho B, Patel S. The Impact of Reducing the Number of Wearable Devices on Measuring Gait in Parkinson Disease: Noninterventional Exploratory Study. JMIR Rehabil Assist Technol 2020; 7:e17986. [PMID: 33084585 PMCID: PMC7641789 DOI: 10.2196/17986] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [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: 01/27/2020] [Revised: 08/07/2020] [Accepted: 08/16/2020] [Indexed: 12/18/2022] Open
Abstract
Background Measuring free-living gait using wearable devices may offer higher granularity and temporal resolution than the current clinical assessments for patients with Parkinson disease (PD). However, increasing the number of devices worn on the body adds to the patient burden and impacts the compliance. Objective This study aimed to investigate the impact of reducing the number of wearable devices on the ability to assess gait impairments in patients with PD. Methods A total of 35 volunteers with PD and 60 healthy volunteers performed a gait task during 2 clinic visits. Participants with PD were assessed in the On and Off medication state using the Movement Disorder Society version of the Unified Parkinson Disease Rating Scale (MDS-UPDRS). Gait features derived from a single lumbar-mounted accelerometer were compared with those derived using 3 and 6 wearable devices for both participants with PD and healthy participants. Results A comparable performance was observed for predicting the MDS-UPDRS gait score using longitudinal mixed-effects model fit with gait features derived from a single (root mean square error [RMSE]=0.64; R2=0.53), 3 (RMSE=0.64; R2=0.54), and 6 devices (RMSE=0.54; R2=0.65). In addition, MDS-UPDRS gait scores predicted using all 3 models differed significantly between On and Off motor states (single device, P=.004; 3 devices, P=.004; 6 devices, P=.045). Conclusions We observed a marginal benefit in using multiple devices for assessing gait impairments in patients with PD when compared with gait features derived using a single lumbar-mounted accelerometer. The wearability burden associated with the use of multiple devices may offset gains in accuracy for monitoring gait under free-living conditions.
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Affiliation(s)
- Matthew Czech
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Charmaine Demanuele
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Michael Kelley Erb
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Vesper Ramos
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Hao Zhang
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Bryan Ho
- Tufts Medical Center, Boston, MA, United States
| | - Shyamal Patel
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
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13
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Czech MD, Psaltos D, Zhang H, Adamusiak T, Calicchio M, Kelekar A, Messere A, Van Dijk KRA, Ramos V, Demanuele C, Cai X, Santamaria M, Patel S, Karahanoglu FI. Age and environment-related differences in gait in healthy adults using wearables. NPJ Digit Med 2020; 3:127. [PMID: 33083562 PMCID: PMC7528045 DOI: 10.1038/s41746-020-00334-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 05/13/2020] [Accepted: 08/31/2020] [Indexed: 11/13/2022] Open
Abstract
Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18-40 years) and older (n = 32, 65-85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.
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Affiliation(s)
- Matthew D. Czech
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | | | - Hao Zhang
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | - Tomasz Adamusiak
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | - Monica Calicchio
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | - Amey Kelekar
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | - Andrew Messere
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | | | - Vesper Ramos
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | | | - Xuemei Cai
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | - Mar Santamaria
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
| | - Shyamal Patel
- Early Clinical Development, Pfizer, Inc., Cambridge, 02139 MA USA
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14
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Erb MK, Karlin DR, Ho BK, Thomas KC, Parisi F, Vergara-Diaz GP, Daneault JF, Wacnik PW, Zhang H, Kangarloo T, Demanuele C, Brooks CR, Detheridge CN, Shaafi Kabiri N, Bhangu JS, Bonato P. mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease. NPJ Digit Med 2020; 3:6. [PMID: 31970291 PMCID: PMC6969057 DOI: 10.1038/s41746-019-0214-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [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: 06/10/2019] [Accepted: 12/05/2019] [Indexed: 11/18/2022] Open
Abstract
Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.
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Affiliation(s)
- M. Kelley Erb
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
| | - Daniel R. Karlin
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
- Department of Psychiatry, Tufts University School of Medicine, Boston, MA USA
| | - Bryan K. Ho
- Department of Neurology, Tufts University School of Medicine, Boston, MA USA
| | - Kevin C. Thomas
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Federico Parisi
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
| | - Gloria P. Vergara-Diaz
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
| | - Jean-Francois Daneault
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
| | - Paul W. Wacnik
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
| | - Hao Zhang
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
| | | | | | - Chris R. Brooks
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Craig N. Detheridge
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Nina Shaafi Kabiri
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Jaspreet S. Bhangu
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
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15
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Mahadevan N, Demanuele C, Zhang H, Volfson D, Ho B, Erb MK, Patel S. Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. NPJ Digit Med 2020; 3:5. [PMID: 31970290 PMCID: PMC6962225 DOI: 10.1038/s41746-019-0217-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [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: 06/19/2019] [Accepted: 12/16/2019] [Indexed: 01/09/2023] Open
Abstract
Objective assessment of Parkinson's disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.
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Affiliation(s)
| | | | - Hao Zhang
- Pfizer, Inc., Cambridge, MA 02139 USA
| | | | - Bryan Ho
- Tufts Medical Center, Boston, MA 02111 USA
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16
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Bartsch U, Simpkin AJ, Demanuele C, Wamsley E, Marston HM, Jones MW. Distributed slow-wave dynamics during sleep predict memory consolidation and its impairment in schizophrenia. NPJ Schizophr 2019; 5:18. [PMID: 31685816 PMCID: PMC6828759 DOI: 10.1038/s41537-019-0086-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 09/17/2019] [Indexed: 11/23/2022]
Abstract
The slow waves (SW) of non-rapid eye movement (NREM) sleep reflect neocortical components of network activity during sleep-dependent information processing; their disruption may therefore impair memory consolidation. Here, we quantify sleep-dependent consolidation of motor sequence memory, alongside sleep EEG-derived SW properties and synchronisation, and SW–spindle coupling in 21 patients suffering from schizophrenia and 19 healthy volunteers. Impaired memory consolidation in patients culminated in an overnight improvement in motor sequence task performance of only 1.6%, compared with 15% in controls. During sleep after learning, SW amplitudes and densities were comparable in healthy controls and patients. However, healthy controls showed a significant 45% increase in frontal-to-occipital SW coherence during sleep after motor learning in comparison with a baseline night (baseline: 0.22 ± 0.05, learning: 0.32 ± 0.05); patient EEG failed to show this increase (baseline: 0.22 ± 0.04, learning: 0.19 ± 0.04). The experience-dependent nesting of spindles in SW was similarly disrupted in patients: frontal-to-occipital SW–spindle phase-amplitude coupling (PAC) significantly increased after learning in healthy controls (modulation index baseline: 0.17 ± 0.02, learning: 0.22 ± 0.02) but not in patients (baseline: 0.13 ± 0.02, learning: 0.14 ± 0.02). Partial least-squares regression modelling of coherence and PAC data from all electrode pairs confirmed distributed SW coherence and SW–spindle coordination as superior predictors of overnight memory consolidation in healthy controls but not in patients. Quantifying the full repertoire of NREM EEG oscillations and their long-range covariance therefore presents learning-dependent changes in distributed SW and spindle coordination as fingerprints of impaired cognition in schizophrenia.
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Affiliation(s)
- Ullrich Bartsch
- Translational & Integrative Neuroscience, Lilly Research Centre, Windlesham, Surrey, GU20 6PH, UK. .,School of Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK.
| | - Andrew J Simpkin
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, H91 TK33, Ireland
| | - Charmaine Demanuele
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, 02215, USA.,Athinoula A. Martinos Centicaer for Biomedl Imaging, Charlestown, MA, 02129, USA.,Harvard Medical School, Boston, MA, 02115, USA.,Early Clinical Development, Pfizer Inc., Cambridge, MA, USA
| | - Erin Wamsley
- Department of Psychology, Furman University, Greenville, SC, 29613, USA
| | - Hugh M Marston
- Translational & Integrative Neuroscience, Lilly Research Centre, Windlesham, Surrey, GU20 6PH, UK
| | - Matthew W Jones
- School of Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK
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17
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Psaltos D, Chappie K, Karahanoglu FI, Chasse R, Demanuele C, Kelekar A, Zhang H, Marquez V, Kangarloo T, Patel S, Czech M, Caouette D, Cai X. Multimodal Wearable Sensors to Measure Gait and Voice. Digit Biomark 2019; 3:133-144. [PMID: 32095772 DOI: 10.1159/000503282] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [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: 07/12/2019] [Revised: 09/10/2019] [Indexed: 12/31/2022] Open
Abstract
Background Traditional measurement systems utilized in clinical trials are limited because they are episodic and thus cannot capture the day-to-day fluctuations and longitudinal changes that frequently affect patients across different therapeutic areas. Objectives The aim of this study was to collect and evaluate data from multiple devices, including wearable sensors, and compare them to standard lab-based instruments across multiple domains of daily tasks. Methods Healthy volunteers aged 18-65 years were recruited for a 1-h study to collect and assess data from wearable sensors. They performed walking tasks on a gait mat while instrumented with a watch, phone, and sensor insoles as well as several speech tasks on multiple recording devices. Results Step count and temporal gait metrics derived from a single lumbar accelerometer are highly precise; spatial gait metrics are consistently 20% shorter than gait mat measurements. The insole's algorithm only captures about 72% of steps but does have precision in measuring temporal gait metrics. Mobile device voice recordings provide similar results to traditional recorders for average signal pitch and sufficient signal-to-noise ratio for analysis when hand-held. Lossless compression techniques are advised for signal processing. Conclusions Gait metrics from a single lumbar accelerometer sensor are in reasonable concordance with standard measurements, with some variation between devices and across individual metrics. Finally, participants in this study were familiar with mobile devices and had high acceptance of potential future continuous wear for clinical trials.
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Affiliation(s)
- Dimitrios Psaltos
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - Kara Chappie
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | | | - Rachel Chasse
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | | | - Amey Kelekar
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - Hao Zhang
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - Vanessa Marquez
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - Tairmae Kangarloo
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - Shyamal Patel
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - Matthew Czech
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - David Caouette
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA
| | - Xuemei Cai
- Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA.,Tufts Medical Center, Boston, Massachusetts, USA
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18
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Mylonas D, Demanuele C, Baran B, Cox R, Stickgold R, Manoach DS. 0915 The Effects of Eszopiclone on Spindles, Slow Oscillations and their Coordination in Health and Schizophrenia. Sleep 2019. [DOI: 10.1093/sleep/zsz067.913] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Dimitris Mylonas
- Harvard Medical School, Boston, MA, Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, USA
| | - Charmaine Demanuele
- Harvard Medical School, Boston, MA, Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, USA
| | - Bengi Baran
- Harvard Medical School, Boston, MA, Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, USA
| | - Roy Cox
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dara S Manoach
- Harvard Medical School, Boston, MA, Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, USA
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19
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Shi Z, Mylonas DS, Khan S, Demanuele C, Zhu L, Tocci C, Hämäläinen MS, Stickgold R, Manoach D. 0098 Local Spindle Increase is Correlated with Sleep-Dependent Memory Consolidation of Motor Sequence Task. Sleep 2019. [DOI: 10.1093/sleep/zsz067.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Zhaoyue Shi
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Dimitris S Mylonas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Charmaine Demanuele
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Lin Zhu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Catherine Tocci
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Robert Stickgold
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Dara Manoach
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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20
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Brooks C, Eden G, Chang A, Demanuele C, Kelley Erb M, Shaafi Kabiri N, Moss M, Bhangu J, Thomas K. Quantification of discrete behavioral components of the MDS-UPDRS. J Clin Neurosci 2018; 61:174-179. [PMID: 30385169 DOI: 10.1016/j.jocn.2018.10.043] [Citation(s) in RCA: 6] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 10/07/2018] [Indexed: 12/15/2022]
Abstract
INTRODUCTION The Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the current gold standard means of assessing disease state in Parkinson's disease (PD). Objective measures in the form of wearable sensors have the potential to improve our ability to monitor symptomology in PD, but numerous methodological challenges remain, including integration into the MDS-UPDRS. We applied a structured video coding scheme to temporally quantify clinical, scripted, motor tasks in the MDS-UPDRS for the alignment and integration of objective measures collected in parallel. METHODS 25 PD subjects completed two video-recorded MDS-UPDRS administrations. Visual cues of task performance reliably identifiable in video recordings were used to construct a structured video coding scheme. Postural transitions were also defined and coded. Videos were independently coded by two trained non-expert coders and a third expert coder to derive indices of inter-rater agreement. RESULTS 50 videos of MDS-UPDRS performance were fully coded. Non-expert coders achieved a high level of agreement (Cohen's κ > 0.8) on all postural transitions and scripted motor tasks except for Postural Stability (κ = 0.617); this level of agreement was largely maintained even when more stringent thresholds for agreement were applied. Durations coded by non-expert coders and expert coders were significantly different (p < 0.05) for only Postural Stability and Rigidity, Left Upper Limb. CONCLUSIONS Non-expert coders consistently and accurately quantified discrete behavioral components of the MDS-UPDRS using a structured video coding scheme; this represents a novel, promising approach for integrating objective and clinical measures into unified, longitudinal datasets.
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Affiliation(s)
- Chris Brooks
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA.
| | - Gabrielle Eden
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Andrew Chang
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | | | | | - Nina Shaafi Kabiri
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Mark Moss
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Jaspreet Bhangu
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Kevin Thomas
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
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21
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Anand V, Bilal E, Ramos V, Naylor M, Demanuele C, Zhang H, Amato S, Wacnik P, Hameed F, Kangarloo T, Ho B, Erb K, Karlin D. F62. Automatic detection of ON/OFF states in Parkinson disease patients using wearable inertial sensors. Clin Neurophysiol 2018. [DOI: 10.1016/j.clinph.2018.04.225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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22
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Coon WG, Mylonas D, Baran B, Demanuele C, Stickgold R, Manoach D. 0096 Sleep Spindle Coherence And Density Predict Sleep-enhanced Learning In Schizophrenia. Sleep 2018. [DOI: 10.1093/sleep/zsy061.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- W G Coon
- Harvard Medical School, Boston, MA
- Massachusetts General Hospital, Charlestown, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
| | - D Mylonas
- Massachusetts General Hospital, Charlestown, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
| | - B Baran
- Harvard Medical School, Boston, MA
- Massachusetts General Hospital, Charlestown, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
| | - C Demanuele
- Harvard Medical School, Boston, MA
- Massachusetts General Hospital, Charlestown, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
| | - R Stickgold
- Massachusetts General Hospital, Charlestown, MA
- Beth Israel Deconess Medical Center, Boston, MA
| | - D Manoach
- Harvard Medical School, Boston, MA
- Massachusetts General Hospital, Charlestown, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
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23
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Demanuele C, Bartsch U, Baran B, Khan S, Vangel MG, Cox R, Hämäläinen M, Jones MW, Stickgold R, Manoach DS. Coordination of Slow Waves With Sleep Spindles Predicts Sleep-Dependent Memory Consolidation in Schizophrenia. Sleep 2017; 40:2739498. [PMID: 28364465 DOI: 10.1093/sleep/zsw013] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2016] [Indexed: 01/21/2023] Open
Abstract
Study Objectives Schizophrenia patients have correlated deficits in sleep spindle density and sleep-dependent memory consolidation. In addition to spindle density, memory consolidation is thought to rely on the precise temporal coordination of spindles with slow waves (SWs). We investigated whether this coordination is intact in schizophrenia and its relation to motor procedural memory consolidation. Methods Twenty-one chronic medicated schizophrenia patients and 17 demographically matched healthy controls underwent two nights of polysomnography, with training on the finger tapping motor sequence task (MST) on the second night and testing the following morning. We detected SWs (0.5-4 Hz) and spindles during non-rapid eye movement (NREM) sleep. We measured SW-spindle phase-amplitude coupling and its relation with overnight improvement in MST performance. Results Patients did not differ from controls in the timing of SW-spindle coupling. In both the groups, spindles peaked during the SW upstate. For patients alone, the later in the SW upstate that spindles peaked and the more reliable this phase relationship, the greater the overnight MST improvement. Regression models that included both spindle density and SW-spindle coordination predicted overnight improvement significantly better than either parameter alone, suggesting that both contribute to memory consolidation. Conclusion Schizophrenia patients show intact spindle-SW temporal coordination, and these timing relationships, together with spindle density, predict sleep-dependent memory consolidation. These relations were seen only in patients suggesting that their memory is more dependent on optimal spindle-SW timing, possibly due to reduced spindle density. Interventions to improve memory may need to increase spindle density while preserving or enhancing the coordination of NREM oscillations.
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Affiliation(s)
- Charmaine Demanuele
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA.,Harvard Medical School, Boston, MA
| | - Ullrich Bartsch
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA.,School of Physiology and Pharmacology, University of Bristol, Bristol, United Kingdom
| | - Bengi Baran
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA.,Harvard Medical School, Boston, MA
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA.,Harvard Medical School, Boston, MA
| | - Mark G Vangel
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA.,Harvard Medical School, Boston, MA
| | - Roy Cox
- Harvard Medical School, Boston, MA.,Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA.,Harvard Medical School, Boston, MA
| | - Matthew W Jones
- School of Physiology and Pharmacology, University of Bristol, Bristol, United Kingdom
| | - Robert Stickgold
- Harvard Medical School, Boston, MA.,Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA.,Harvard Medical School, Boston, MA
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24
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Purcell SM, Manoach DS, Demanuele C, Cade BE, Mariani S, Cox R, Panagiotaropoulou G, Saxena R, Pan JQ, Smoller JW, Redline S, Stickgold R. Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat Commun 2017. [PMID: 28649997 PMCID: PMC5490197 DOI: 10.1038/ncomms15930] [Citation(s) in RCA: 212] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders. Sleep patterns vary and are associated with health and disease. Here Purcell et al characterize sleep spindle activity in 11,630 individuals and describe age-related changes, genetic influences, and possible confounding effects, serving as a resource for further understanding the physiology of sleep.
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Affiliation(s)
- S M Purcell
- Department of Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - D S Manoach
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129, USA
| | - C Demanuele
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129, USA
| | - B E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - S Mariani
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - R Cox
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, USA
| | - G Panagiotaropoulou
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129, USA
| | - R Saxena
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
| | - J Q Pan
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - J W Smoller
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - S Redline
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - R Stickgold
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, USA
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25
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Mylonas DS, Demanuele C, Baran B, Kohnke EJ, Tocci C, Stickgold R, Hamalainen M, Manoach DS. 1126 SPINDLE ACTIVITY RELATED TO MOTOR PROCEDURAL LEARNING IN PATIENTS WITH SCHIZOPHRENIA. Sleep 2017. [DOI: 10.1093/sleepj/zsx050.1125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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26
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Baran B, Demanuele C, Vuper TC, Seicol B, Fowler RA, Correll D, Parr E, Callahan CE, Morgan A, Stickgold R, Manoach DS. 1113 THE EFFECTS OF ESZOPICLONE ON SLEEP SPINDLES AND MEMORY CONSOLIDATION IN SCHIZOPHRENIA: A DOUBLE-BLIND RANDOMIZED TRIAL. Sleep 2017. [DOI: 10.1093/sleepj/zsx050.1112] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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27
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Demanuele C, Bähner F, Plichta MM, Kirsch P, Tost H, Meyer-Lindenberg A, Durstewitz D. A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series. Front Hum Neurosci 2015; 9:537. [PMID: 26557064 PMCID: PMC4617410 DOI: 10.3389/fnhum.2015.00537] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [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: 06/26/2015] [Accepted: 09/14/2015] [Indexed: 11/17/2022] Open
Abstract
Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.
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Affiliation(s)
- Charmaine Demanuele
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany ; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany ; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School Boston, MA, USA
| | - Florian Bähner
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany ; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany
| | - Michael M Plichta
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany
| | - Peter Kirsch
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany ; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University Mannheim, Germany
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28
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Bähner F, Demanuele C, Schweiger J, Gerchen MF, Zamoscik V, Ueltzhöffer K, Hahn T, Meyer P, Flor H, Durstewitz D, Tost H, Kirsch P, Plichta MM, Meyer-Lindenberg A. Hippocampal-dorsolateral prefrontal coupling as a species-conserved cognitive mechanism: a human translational imaging study. Neuropsychopharmacology 2015; 40:1674-81. [PMID: 25578799 PMCID: PMC4915249 DOI: 10.1038/npp.2015.13] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 12/01/2014] [Accepted: 12/22/2014] [Indexed: 12/30/2022]
Abstract
Hippocampal-prefrontal cortex (HC-PFC) interactions are implicated in working memory (WM) and altered in psychiatric conditions with cognitive impairment such as schizophrenia. While coupling between both structures is crucial for WM performance in rodents, evidence from human studies is conflicting and translation of findings is complicated by the use of differing paradigms across species. We therefore used functional magnetic resonance imaging together with a spatial WM paradigm adapted from rodent research to examine HC-PFC coupling in humans. A PFC-parietal network was functionally connected to hippocampus (HC) during task stages requiring high levels of executive control but not during a matched control condition. The magnitude of coupling in a network comprising HC, bilateral dorsolateral PFC (DLPFC), and right supramarginal gyrus explained one-fourth of the variability in an independent spatial WM task but was unrelated to visual WM performance. HC-DLPFC coupling may thus represent a systems-level mechanism specific to spatial WM that is conserved across species, suggesting its utility for modeling cognitive dysfunction in translational neuroscience.
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Affiliation(s)
- Florian Bähner
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Charmaine Demanuele
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Janina Schweiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Martin F Gerchen
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Vera Zamoscik
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kai Ueltzhöffer
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychology, Goethe University, Frankfurt am Main, Germany
| | - Tim Hahn
- Department of Psychology, Goethe University, Frankfurt am Main, Germany
| | - Patric Meyer
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Herta Flor
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Durstewitz
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Peter Kirsch
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Michael M Plichta
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5, Mannheim 68159, Germany, Tel: +49 621 1703 2001, Fax: +49 621 1703 2005, E-mail:
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29
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Bilek E, Ruf M, Schäfer A, Akdeniz C, Calhoun VD, Schmahl C, Demanuele C, Tost H, Kirsch P, Meyer-Lindenberg A. Information flow between interacting human brains: Identification, validation, and relationship to social expertise. Proc Natl Acad Sci U S A 2015; 112:5207-12. [PMID: 25848050 PMCID: PMC4413334 DOI: 10.1073/pnas.1421831112] [Citation(s) in RCA: 99] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Social interactions are fundamental for human behavior, but the quantification of their neural underpinnings remains challenging. Here, we used hyperscanning functional MRI (fMRI) to study information flow between brains of human dyads during real-time social interaction in a joint attention paradigm. In a hardware setup enabling immersive audiovisual interaction of subjects in linked fMRI scanners, we characterize cross-brain connectivity components that are unique to interacting individuals, identifying information flow between the sender's and receiver's temporoparietal junction. We replicate these findings in an independent sample and validate our methods by demonstrating that cross-brain connectivity relates to a key real-world measure of social behavior. Together, our findings support a central role of human-specific cortical areas in the brain dynamics of dyadic interactions and provide an approach for the noninvasive examination of the neural basis of healthy and disturbed human social behavior with minimal a priori assumptions.
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Affiliation(s)
- Edda Bilek
- Departments of Psychiatry and Psychotherapy
| | | | | | | | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM 87131; and Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131
| | | | | | - Heike Tost
- Departments of Psychiatry and Psychotherapy
| | - Peter Kirsch
- Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
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30
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Manoach DS, Demanuele C, Wamsley EJ, Vangel M, Montrose DM, Miewald J, Kupfer D, Buysse D, Stickgold R, Keshavan MS. Sleep spindle deficits in antipsychotic-naïve early course schizophrenia and in non-psychotic first-degree relatives. Front Hum Neurosci 2014; 8:762. [PMID: 25339881 PMCID: PMC4188028 DOI: 10.3389/fnhum.2014.00762] [Citation(s) in RCA: 107] [Impact Index Per Article: 10.7] [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: 05/23/2014] [Accepted: 09/09/2014] [Indexed: 12/31/2022] Open
Abstract
INTRODUCTION Chronic medicated patients with schizophrenia have marked reductions in sleep spindle activity and a correlated deficit in sleep-dependent memory consolidation. Using archival data, we investigated whether antipsychotic-naïve early course patients with schizophrenia and young non-psychotic first-degree relatives of patients with schizophrenia also show reduced sleep spindle activity and whether spindle activity correlates with cognitive function and symptoms. METHOD Sleep spindles during Stage 2 sleep were compared in antipsychotic-naïve adults newly diagnosed with psychosis, young non-psychotic first-degree relatives of schizophrenia patients and two samples of healthy controls matched to the patients and relatives. The relations of spindle parameters with cognitive measures and symptom ratings were examined. RESULTS Early course schizophrenia patients showed significantly reduced spindle activity relative to healthy controls and to early course patients with other psychotic disorders. Relatives of schizophrenia patients also showed reduced spindle activity compared with controls. Reduced spindle activity correlated with measures of executive function in early course patients, positive symptoms in schizophrenia and IQ estimates across groups. CONCLUSIONS Like chronic medicated schizophrenia patients, antipsychotic-naïve early course schizophrenia patients and young non-psychotic relatives of individuals with schizophrenia have reduced sleep spindle activity. These findings indicate that the spindle deficit is not an antipsychotic side-effect or a general feature of psychosis. Instead, the spindle deficit may predate the onset of schizophrenia, persist throughout its course and be an endophenotype that contributes to cognitive dysfunction.
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Affiliation(s)
- Dara S. Manoach
- Department of Psychiatry, Massachusetts General HospitalCharlestown, MA, USA
- Athinoula A. Martinos Center for Biomedical ImagingCharlestown, MA, USA
- Harvard Medical SchoolBoston, MA, USA
| | - Charmaine Demanuele
- Department of Psychiatry, Massachusetts General HospitalCharlestown, MA, USA
- Athinoula A. Martinos Center for Biomedical ImagingCharlestown, MA, USA
- Harvard Medical SchoolBoston, MA, USA
| | - Erin J. Wamsley
- Harvard Medical SchoolBoston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical CenterBoston, MA, USA
| | - Mark Vangel
- Athinoula A. Martinos Center for Biomedical ImagingCharlestown, MA, USA
- Harvard Medical SchoolBoston, MA, USA
| | - Debra M. Montrose
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of MedicinePittsburgh, PA, USA
| | - Jean Miewald
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of MedicinePittsburgh, PA, USA
| | - David Kupfer
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of MedicinePittsburgh, PA, USA
| | - Daniel Buysse
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of MedicinePittsburgh, PA, USA
| | - Robert Stickgold
- Harvard Medical SchoolBoston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical CenterBoston, MA, USA
| | - Matcheri S. Keshavan
- Harvard Medical SchoolBoston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical CenterBoston, MA, USA
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of MedicinePittsburgh, PA, USA
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Demanuele C, Broyd SJ, Sonuga-Barke EJS, James C. Neuronal oscillations in the EEG under varying cognitive load: a comparative study between slow waves and faster oscillations. Clin Neurophysiol 2012; 124:247-62. [PMID: 22986283 DOI: 10.1016/j.clinph.2012.07.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2012] [Revised: 07/14/2012] [Accepted: 07/18/2012] [Indexed: 12/19/2022]
Abstract
OBJECTIVE This study has been specifically designed to investigate very low frequency neuronal oscillations (VLFO, <0.5 Hz) during resting states and during goal-directed tasks of graded difficulty levels, quantify the changes that the slow waves undergo in these conditions and compare them with those for higher frequency bands (namely delta, theta and alpha). METHODS To this end we developed a multistage signal processing methodology comprising blind source separation coupled with a neural network based feature extraction and classification method. RESULTS Changes in the amplitude and phase of brain sources estimates in the VLF band between rest and task were enhanced with increased task difficulty, but remained lower than those experienced in higher frequency bands. The slow wave variations were also significantly correlated with task performance measures, and hence with the level of task-directed attention. CONCLUSIONS These findings suggest that besides their prominent sensitivity to external stimulation, VLFOs also contribute to the cortical ongoing background activity which may not be specifically related to task-specific attention and performance. SIGNIFICANCE Our work provides important insight into the association between VLF brain activity and conventional EEG frequency bands, and presents a novel framework for assessing neural activity during various mental conditions and psychiatric states.
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Affiliation(s)
- Charmaine Demanuele
- Signal Processing and Control Group, Institute of Sound & Vibration Research, University of Southampton, UK.
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32
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James CJ, Demanuele C. Space-time independent component analysis: the definitive BSS technique to use in biomedical signal processing? Annu Int Conf IEEE Eng Med Biol Soc 2010; 2010:1898-901. [PMID: 21096568 DOI: 10.1109/iembs.2010.5627351] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Independent Component Analysis (ICA) is a very common instantiation of the Blind Source Separation (BSS) problem. In the context of biomedical signal analysis, ICA is generally applied to multi-channel recordings of physiological phenomena in order to de-noise and extract meaningful information underlying the recordings. This paper assesses the Spatio-Temporal ICA (ST-ICA) framework, which uses both spatial and temporal information derived from multi-channel time-series to extract underlying sources. In contrast, the standard implementation of the ICA algorithm generally uses only limited spatial information to inform the separation process. One of the major steps in the implementation of any ICA algorithm is the selection of relevant components from the many ICA usually returns. With ST-ICA there is a rich data-set of components exhibiting spatial as well as temporal/spectral information that could be used to identify the underlying process subspaces extracted by the ST-ICA algorithm. This paper highlights the methodology for performing ST-ICA and assesses the possible ways in which process subspace identification may take place.
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Affiliation(s)
- Christopher J James
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, CV4 7AL, UK.
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Demanuele C, Capilla A, Pérez Hernández E, Sonuga-Barke EJS, James C. Trial-to-Trial Variability in Evoked Neural Responses Exhibits a Very Low Frequency Temporal Signature. J PSYCHOPHYSIOL 2010. [DOI: 10.1027/0269-8803/a000002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In functional magnetic resonance (fMRI) studies, the blood oxygen level dependent (BOLD) signal displays intrinsic spontaneous and task-independent very low frequency (VLF) oscillations (< 0.1 Hz). Most prominent during rest, when they persist into task sessions they can predict trial-to-trial variability in both evoked behavior and brain responses by providing a baseline onto which deterministic responses elicited by the task are superimposed. Moreover, evidence in the literature tentatively suggests that this VLF activity may not be present in the data as distinct, independent source(s) per se, but rather as a mechanism that modulates and perhaps even governs underlying brain processes. Here, we use electrophysiology to investigate the intertrial variability observed in magnetoencephalographic (MEG) event-related field (ERF) components, and to examine whether this variability exhibits a VLF time signature in order to indirectly infer information about the underlying slow waves. The focus is on the visual component, the M100, understood to be regulated by attention. We also explored whether individual differences in the M100 VLF pattern varies as a function of attention deficit/hyperactivity disorder (ADHD) by comparing 11 cases against 11 controls. The M100 component was extracted from the data using a recently introduced blind-source separation technique – space-time independent component analysis (ST-ICA) – which allowed trial-by-trial analysis to be performed on the M100 for proper assessment of VLF modulation. Our results demonstrate, for the first time, the ability of this signal-processing method to isolate relevant components from multidimensional, noisy, ERF data recorded from a highly dense 148-channel MEG system. The intertrial variability in the amplitude and latency of the M100 responses exhibits a slow wave pattern (< 0.1 Hz). However, there was no evidence that the degree of VLF modulation was different in ADHD participants. The role of this VLF activity in brain function is discussed.
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Affiliation(s)
- C. Demanuele
- Signal Processing and Control Group, ISVR, University of Southampton, UK
| | - A. Capilla
- Department of Psychology, University of Glasgow, UK
| | - E. Pérez Hernández
- Developmental and Educational Psychology, School of Education, Complutense University of Madrid, Spain
| | - E. J. S. Sonuga-Barke
- Developmental and Brain Behavior Unit, School of Psychology, University of Southampton, UK
- Department of Experimental Clinical and Health Psychology, Ghent University, Belgium
| | - C. James
- Signal Processing and Control Group, ISVR, University of Southampton, UK
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Demanuele C, Sonuga-Barke EJS, James CJ. Slow neuronal oscillations in the resting brain vs task execution: A BSS investigation of EEG recordings. Annu Int Conf IEEE Eng Med Biol Soc 2010; 2010:1638-1641. [PMID: 21096137 DOI: 10.1109/iembs.2010.5626645] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Spontaneous very low frequency oscillations (< 0.5 Hz) occurring within widely distributed neuroanatomical systems have been increasingly analyzed in brain imaging studies. Whilst being more prominent in the resting brain, these slow waves also persist into task sessions and may potentially interfere with active goal-directed attention, leading to periodic lapses in attention during task execution. This work presents a new experimental framework and a multistage signal processing methodology - comprising blind source separation (BSS) coupled with a neural network feature extraction and classification method - developed for assessing variations in the slow wave characteristics in EEG data recorded during periods of quiet wakefulness (termed as "rest"), and during visual tasks of various difficulty levels. Core results demonstrate that the amplitude and phase of the brain sources in the slow wave band share essential similarities during rest and task conditions, but are distinct enough to be classified separately. These slow wave variations are also significantly correlated with the level of cognitive attention assessed by task performance measures (such as reaction times and error rates). Moreover, the power of the brain sources in the slow wave band is attenuated during task, and the level of attenuation drops as the task difficulty level is increased, whilst the slow wave phase undergoes a change in structure (measured through entropy). The methodology and findings presented here provide a new basis for assessing neural activity during various mental conditions.
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Affiliation(s)
- Charmaine Demanuele
- Signal Processing and Control Group, ISVR, University of Southampton, SO17 1BJ, UK.
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James CJ, Demanuele C. On spatio-temporal component selection in space-time independent component analysis: an application to ictal EEG. Annu Int Conf IEEE Eng Med Biol Soc 2009; 2009:3154-7. [PMID: 19964610 DOI: 10.1109/iembs.2009.5334034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper assesses the use of Independent Component Analysis (ICA) as applied to epileptic scalp electroencephalographic (EEG) recordings. In particular we address the newly introduced Spatio-Temporal ICA algorithm (ST-ICA), which uses both spatial and temporal information derived from multi-channel biomedical signal recordings to inform (or update) the standard ICA algorithm. ICA is a technique well suited to extracting underlying sources from multi-channel EEG recordings - for ictal EEG recordings, the goal is to both de-noise the EEG recordings (i.e. remove artifacts) as well as isolate and extract epileptic processes. As part of any ICA application, there is an interim stage whereby relevant components (or processes) need to be identified - either objectively or subjectively (usually the latter). In previous work with ST-ICA we used spectral information alone to identify the underlying processes subspaces extracted by the ST-ICA. Here we assess the joint use of spatial as well as spectral information for this purpose. We test this on ictal EEG segments where it can be seen that different underlying processes possess characteristic signatures in both modalities which can be utilized for the clustering (or process selection) stage.
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Affiliation(s)
- Christopher J James
- Signal Processing and Control Group, ISVR, University of Southampton, SO17 1BJ, UK.
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Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJS. Default-mode brain dysfunction in mental disorders: A systematic review. Neurosci Biobehav Rev 2009; 33:279-96. [PMID: 18824195 DOI: 10.1016/j.neubiorev.2008.09.002] [Citation(s) in RCA: 1136] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2008] [Revised: 09/01/2008] [Accepted: 09/03/2008] [Indexed: 11/17/2022]
Affiliation(s)
- Samantha J Broyd
- Institute for Disorders of Impulse & Attention, Developmental Brain-Behaviour Laboratory, School of Psychology, University of Southampton, UK.
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Demanuele C, James CJ, Sonuga-Barke EJ. Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals. Behav Brain Funct 2007; 3:62. [PMID: 18070337 PMCID: PMC2235870 DOI: 10.1186/1744-9081-3-62] [Citation(s) in RCA: 50] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2007] [Accepted: 12/10/2007] [Indexed: 11/25/2022] Open
Abstract
Background It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency. This spectral behaviour may lead to difficulty in distinguishing event-related peaks from ongoing brain activity in the electro- and magnetoencephalographic (EEG and MEG) signal spectra. This can become an issue especially in the analysis of low frequency oscillations (LFOs) – below 0.5 Hz – which are currently being observed in signal recordings linked with specific pathologies such as epileptic seizures or attention deficit hyperactivity disorder (ADHD), in sleep studies, etc. Methods In this work we propose a simple method that can be used to compensate for this 1/f trend hence achieving spectral normalisation. This method involves filtering the raw measured EM signal through a differentiator prior to further data analysis. Results Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and Evoked Response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed. Conclusion Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.
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Affiliation(s)
- Charmaine Demanuele
- Signal Processing and Control Group, Institute of Sound and Vibration Research, University of Southampton, Southampton, UK.
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