- Boston, Massachusetts, United States
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... more
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 a...
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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... more
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 predictin...
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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... more
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 se...
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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... more
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 describ...
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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... more
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, ...
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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... more
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...
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The slow-waves (SW) of non-rapid eye movement sleep (NREM) reflect neocortical components of network activity during sleep-dependent information processing; their disruption may therefore contribute to impaired memory consolidation. Here,... more
The slow-waves (SW) of non-rapid eye movement sleep (NREM) reflect neocortical components of network activity during sleep-dependent information processing; their disruption may therefore contribute to impaired memory consolidation. Here, we quantify SW dynamics relative to motor sequence memory in patients suffering schizophrenia and healthy volunteers. Patients showed normal intrinsic SW properties but impaired SW coherence, which failed to exhibit the learning-dependent increases evident in healthy volunteers. SW-spindle phase amplitude coupling across distributed EEG electrodes was also dissociated from experience in patients, with long-range fronto-parietal and -occipital networks most severely affected. Partial least squares regression modelling confirmed distributed SW coherence and SW-spindle coordination as predictors of overnight memory consolidation in healthy controls, but not in patients. Quantifying the full repertoire of NREM EEG oscillations and their long-range cova...
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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... more
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. 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. 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, t...
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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... more
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 dis...
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Research Interests: Psychology, Schizophrenia, Cognition, Medicine, Sleep, and 5 moreIQ, Polysomnography, Relatives, Sleep Spindles, and Endophenotype
ABSTRACT Event-related studies have provided indirect evidence that Attention Deficit Hyperactivity Disorder (ADHD) children have abnormalities in signal detection and discrimination, and in information processing. Moreover, studies... more
ABSTRACT Event-related studies have provided indirect evidence that Attention Deficit Hyperactivity Disorder (ADHD) children have abnormalities in signal detection and discrimination, and in information processing. Moreover, studies suggest that there exist very low frequency fluctuations modulating underlying neuronal events. This paper presents an event-related fields (ERF) study involving the analysis of magnetoencephalographic (MEG) recordings of children with ADHD and controls during selective attention and perceptual, stimuli-based tasks. A specific form of blind-source separation — space-time independent component analysis (ST-ICA) — is used to isolate the early M100 responses within the data, which are indicative of selective attention deficits. The properties of the extracted responses, namely the amplitude and latency, as well as the power spectral densities of their inter-trial variations are then analyzed. Preliminary results demonstrate the ability of ST-ICA to extract relevant components from multi-dimensional, noisy, ERF data, and reveal differences in the amplitude and latency variations of the M100 responses of the two groups.
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Research Interests: Neuroscience, Cognition, Magnetic Resonance Imaging, Neuropsychopharmacology, Prefrontal Cortex, and 15 moreBrain Mapping, Hippocampus, Humans, Functional Magnetic Resonance Imaging, Female, Male, Computer User Interface Design, Oxygen, Adult, Neural pathways, Dorsolateral Prefrontal Cortex, Hippocampal formation, Functional Laterality, Neuropsychological Tests, and Medical and Health Sciences
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Spontaneous very low frequency oscillations (<0.5 Hz), previously regarded as physiological noise, have of late been increasingly analysed in neuroimaging studies. These slow oscillations, which occur within widely distributed... more
Spontaneous very low frequency oscillations (<0.5 Hz), previously regarded as physiological noise, have of late been increasingly analysed in neuroimaging studies. These slow oscillations, which occur within widely distributed neuroanatomical systems and are ...
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Research Interests: Computer Science, Artificial Intelligence, Decision Making, Cognition, Magnetic Resonance Imaging, and 15 moreMedicine, Multidisciplinary, Multivariate Analysis, Biological Sciences, Information, Brain Mapping, Humans, DLPFC, Female, Male, Acc, Adult, Brain Regions, Dorsolateral Prefrontal Cortex, and Feature extraction algorithm
Research Interests: Neuroscience and Medicine
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... more
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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In functional magnetic resonance (fMRI) studies, the blood oxygen level dependent (BOLD) signal displays intrinsic spontaneous and task-independent very low frequency (VLF) oscillations (&amp;amp;lt; 0.1 Hz). Most prominent during... more
In functional magnetic resonance (fMRI) studies, the blood oxygen level dependent (BOLD) signal displays intrinsic spontaneous and task-independent very low frequency (VLF) oscillations (&amp;amp;lt; 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
Research Interests: Psychology, Cognitive Science, Psychophysiology, Signal Processing, Attention-Deficit/Hyperactivity Disorder, and 10 moreMagnetoencephalography, Independent Component Analysis, Blind Source Separation, Very Low Frequency Electromagnetics, Magnetic Resonance, Space Time, Oscillations, Individual Difference, Brain Function, and Neurosciences
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... more
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.
Research Interests: Algorithms, Biomedical Engineering, Electrophysiology, Principal Component Analysis, Independent Component Analysis, and 10 moreElectroencephalography, Blind Source Separation, Humans, Cluster Analysis, Spatial Information, Temporal Information Extraction, Head, Time Factors, Equipment Design, and Subspace Identification
Research Interests: Neuroscience, Attention-Deficit/Hyperactivity Disorder, Cognition, Magnetoencephalography, Magnetic Resonance Imaging, and 14 moreElectroencephalography, Low Frequency, Functional Connectivity, Brain, Humans, Default Mode Network, Mental Disorders, Oscillations, Task Performance, Mental Disorder, Systematic review, Cognitive Function, Resting State, and Low Frequency Oscillation
Spontaneous very low frequency oscillations (&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; 0.5 Hz) occurring within widely distributed neuroanatomical... more
Spontaneous very low frequency oscillations (&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; 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 &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;rest&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;), 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.
Research Interests: Algorithms, Brain Imaging, Signal Processing, Visual perception, Electroencephalography, and 20 moreBlind Source Separation, Neural Network, Medicine, Very Low Frequency Electromagnetics, Brain, Humans, Computer Simulation, Female, Male, Reaction Time, Young Adult, Feature Extraction, Bit Error Rate, Oscillations, Task Performance, Task Difficulty, Rest, Adult, Biological clocks, and Task Performance and Analysis
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Issues on the Design of a Variable Power LINC ... Amplifier System in SiGe Operating at 2.4 GHz ... Charmaine Demanuele, Ivan Grech, Joseph Micallef, Edward Gatt ... Department of Electronic Systems Engineering, University of Malta, Msida... more
Issues on the Design of a Variable Power LINC ... Amplifier System in SiGe Operating at 2.4 GHz ... Charmaine Demanuele, Ivan Grech, Joseph Micallef, Edward Gatt ... Department of Electronic Systems Engineering, University of Malta, Msida MSD 06, Malta. Tel.: +356 2340 2075, ...