Alessandro Presacco

University of Maryland, College Park, CGS, Maryland, United States

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Publications (10)15.02 Total impact

  • Alessandro Presacco · Kimberly Jenkins · Rachel Lieberman · Samira Anderson
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    ABSTRACT: The authors investigated aging effects on the envelope of the frequency following response to dynamic and static components of speech. Older adults frequently experience problems understanding speech, despite having clinically normal hearing. Improving audibility with hearing aids provides variable benefit, as amplification cannot restore the temporal precision degraded by aging. Previous studies have demonstrated age-related delays in subcortical timing specific to the dynamic, transition region of the stimulus. However, it is unknown whether this delay is mainly due to a failure to encode rapid changes in the formant transition because of central temporal processing deficits or as a result of cochlear damage that reduces audibility for the high-frequency components of the speech syllable. To investigate the nature of this delay, the authors compared subcortical responses in younger and older adults with normal hearing to the speech syllables /da/ and /a/, hypothesizing that the delays in peak timing observed in older adults are mainly caused by temporal processing deficits in the central auditory system. The frequency following response was recorded to the speech syllables /da/ and /a/ from 15 younger and 15 older adults with normal hearing, normal IQ, and no history of neurological disorders. Both speech syllables were presented binaurally with alternating polarities at 80 dB SPL at a rate of 4.3 Hz through electromagnetically shielded insert earphones. A vertical montage of four Ag-AgCl electrodes (Cz, active, forehead ground, and earlobe references) was used. The responses of older adults were significantly delayed with respect to younger adults for the transition and onset regions of the /da/ syllable and for the onset of the /a/ syllable. However, in contrast with the younger adults who had earlier latencies for /da/ than for /a/ (as was expected given the high-frequency energy in the /da/ stop consonant burst), latencies in older adults were not significantly different between the responses to /da/ and /a/. An unexpected finding was noted in the amplitude and phase dissimilarities between the two groups in the later part of the steady-state region, rather than in the transition region. This amplitude reduction may indicate prolonged neural recovery or response decay associated with a loss of auditory nerve fibers. These results suggest that older adults' peak timing delays may arise from decreased synchronization to the onset of the stimulus due to reduced audibility, though the possible role of impaired central auditory processing cannot be ruled out. Conversely, a deterioration in temporal processing mechanisms in the auditory nerve, brainstem, or midbrain may be a factor in the sudden loss of synchronization in the later part of the steady-state response in older adults.
    No preview · Article · Jul 2015 · Ear and hearing
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    Alessandro Presacco · Larry W Forrester · Jose L Contreras-Vidal
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    ABSTRACT: Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra- and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was ~0.68±0.08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis.
    Full-text · Article · Mar 2012 · IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society
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    ABSTRACT: This article highlights recent advances in the design of noninvasive neural interfaces based on the scalp electroencephalogram (EEG). The simplest of physical tasks, such as turning the page to read this article, requires an intense burst of brain activity. It happens in milliseconds and requires little conscious thought. But for amputees and stroke victims with diminished motor-sensory skills, this process can be difficult or impossible. Our team at the University of Maryland, in conjunction with the Johns Hopkins Applied Physics Laboratory (APL) and the University of Maryland School of Medicine, hopes to offer these people newfound mobility and dexterity. In separate research thrusts, were using data gleaned from scalp EEG to develop reliable brainmachine interface (BMI) systems that could soon control modern devices such as prosthetic limbs or powered robotic exoskeletons.
    Full-text · Article · Jan 2012 · IEEE Pulse
  • Alessandro Presacco · Larry Forrester · Jose L Contreras-Vidal
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    ABSTRACT: Before 2009, the feasibility of applying brain-machine interfaces (BMIs) to control prosthetic devices had been limited to upper limb prosthetics such as the DARPA modular prosthetic limb. Until recently, it was believed that the control of bipedal locomotion involved central pattern generators with little supraspinal control. Analysis of cortical dynamics with electroencephalography (EEG) was also prevented by the lack of analysis tools to deal with excessive signal artifacts associated with walking. Recently, Nicolelis and colleagues paved the way for the decoding of locomotion showing that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to decode bipedal kinematics in rhesus monkeys. However, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This study uses non-invasive EEG signals to decode human walking in six nondisabled adults. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs, to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular kinematics of the left and right hip, knee and ankle joints and EEG were recorded concurrently. Our results support the possibility of decoding human bipedal locomotion with EEG. The average of the correlation values (r) between predicted and recorded kinematics for the six subjects was 0.7 (± 0.12) for the right leg and 0.66 (± 0.11) for the left leg. The average signal-to-noise ratio (SNR) values for the predicted parameters were 3.36 (± 1.89) dB for the right leg and 2.79 (± 1.33) dB for the left leg. These results show the feasibility of developing non-invasive neural interfaces for volitional control of devices aimed at restoring human gait function.
    No preview · Article · Aug 2011 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
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    ABSTRACT: Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function.
    Full-text · Article · Jul 2011 · Journal of Neurophysiology
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    ABSTRACT: Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebellar ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. However, open-loop reinforcement of motor control programs may hold promise as a technique to improve motor performance. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG-based BCI provide advanced biofeedback to improve motor imagery and provide a “backdoor” to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.
    No preview · Conference Paper · Apr 2011
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    Shrivats Iyer · Anil Maybhate · Alessandro Presacco · Angelo H All
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    ABSTRACT: The motor evoked potential (MEP) is an electrical response of peripheral neuro-muscular pathways to stimulation of the motor cortex. MEPs provide objective assessment of electrical conduction through the associated neural pathways, and therefore detect disruption due to a nervous system injury such as spinal cord injury (SCI). In our studies of SCI, we developed a novel, multi-channel set-up for MEP acquisition in rat models. Unlike existing electrophysiological systems for SCI assessment, the set-up allows for multi-channel MEP acquisition from all limbs of rats and enables longitudinal monitoring of injury and treatment for in vivo models of experimental SCI. The article describes the development of the set-up and discusses its capabilities to acquire MEPs in rat models of SCI. We demonstrate its use for MEP acquisition under two types of anesthesia as well as a range of cortical stimulation parameters, identifying parameters yielding consistent and reliable MEPs. To validate our set-up, MEPs were recorded from a group of 10 rats before and after contusive SCI. Upon contusion with moderate severity (12.5mm impact height), MEP amplitude decreased by 91.36±6.03%. A corresponding decline of 93.8±11.4% was seen in the motor behavioral score (BBB), a gold standard in rodent models of SCI.
    Preview · Article · Nov 2010 · Journal of Neuroscience Methods
  • Alessandro Presacco · Jorge Bohórquez · Erdem Yavuz · Ozcan Ozdamar
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    ABSTRACT: The nature of the auditory steady-state responses (ASSR) evoked with 40-Hz click trains and their relationship to auditory brainstem and middle latency responses (ABR/MLR), gamma band responses (GBR) and beta band responses (BBR) were investigated using superposition theory. Transient responses obtained by continuous loop averaging deconvolution (CLAD) and last click responses (LCR) were used to synthesize ASSRs and GBRs. ASSRs were obtained with trains of low jittered 40 Hz clicks presented monaurally and deconvolved using a modified CLAD. Resulting transient responses and modified LCRs were used to predict the ASSRs and the GBR. The ABR/MLR obtained with deconvolution predicted accurately the steady portion of the ASSR but failed to predict its onset portion. The modified LCR failed to fully predict both portions. The GBRs were predicted by narrow band filtering of the ASSRs. Significant BBR activity was found both in the ASSRs and deconvolved ABR/MLRs. Simulations using deconvolved ABR/MLRs obtained at 40 Hz predict fully the steady state but not the onset portion of the ASSRs, thus confirming the superposition theory. Click rate adaptation plays a significant role in ASSR generation with click trains and should be considered in evaluating convolved response generation theories.
    No preview · Article · Apr 2010 · Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology
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    ABSTRACT: In healthy humans, the cortical brain rhythm (or electroencephalogram, EEG), shows specific mu (∼8-12 Hz) and beta (∼16-24 Hz) band patterns in the cases of both real and imaginary motor movements. As cerebellar ataxia is associated with impairment of precise motor movement control as well as motor imagery, ataxia is an ideal model system in which to study the role of the cerebellocortical circuit in rhythm control. We hypothesize that the EEG characteristics of ataxic patients differ from those of controls during the performance of a Brain-Computer Interface (BCI) task. EEGs were recorded from four patients with cerebellar ataxia and six healthy controls. Subjects were cued to imagine relaxation or motor movement, while an EEG-based BCI translated motor intention into a visual feedback signal through real-time detection of motor imagery states. Ataxia and control subjects showed a similar distribution of mu power during cued relaxation. During cued motor imagery, however, the ataxia group showed significant spatial distribution of the response, while the control group showed the expected decrease in mu-band power (localized to the motor cortex). This pilot study suggests that impairment of the cerebellocortical control network is associated with spatial spreading of the normal event-related synchronization of motor cortical areas during relaxation. The mechanism of this association, whether degenerative or compensatory, bears further investigation. Use of BCI has important implications for our basic understanding of motor imagery and control, as well as clinical development of brain-computer interface as an assistive or rehabilitative technology.
    No preview · Article · Jan 2010 · IFMBE proceedings
  • A. Presacco · J. Bohórquez · E. Yavuz · Ö. Özdamar
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    ABSTRACT: Continuous Loop Averaging Deconvolution (CLAD) is a recently developed mathematical theory and algorithm that allows to deconvolve averaged electrophysiological signals obtained at high stimulation (Delgado and Özdamar (2004) [1]). It assumes that the individual unit responses are linearly combined to form quasi-steady state responses. One limitation of this algorithm is related to the fact that the length of the recording window has to be equivalent to the duration of the response. In this study, we have developed a modified deconvolution averaging algorithm to derive deconvolved responses when the length of the recording is longer than the duration of the response. The algorithm uses the Least Mean Square (LMS) optimization method to find the deconvolved recording that best fits the acquired data. The method has been applied to study the evoked activity in 40 Hz auditory responses.
    No preview · Article · Jan 2009 · IFMBE proceedings

Publication Stats

117 Citations
15.02 Total Impact Points

Institutions

  • 2011-2015
    • University of Maryland, College Park
      • Department of Kinesiology
      CGS, Maryland, United States
    • Loyola University Maryland
      Baltimore, Maryland, United States
  • 2012
    • University of Houston
      • Department of Electrical & Computer Engineering
      Houston, Texas, United States
  • 2010
    • University of Miami
      • Department of Biomedical Engineering
      كورال غيبلز، فلوريدا, Florida, United States
    • Johns Hopkins University
      • Department of Biomedical Engineering
      Baltimore, Maryland, United States