<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kim, Nayoung</style></author><author><style face="normal" font="default" size="100%">Jamison, Keith</style></author><author><style face="normal" font="default" size="100%">Jaywant, Abhishek</style></author><author><style face="normal" font="default" size="100%">Garetti, Jacob</style></author><author><style face="normal" font="default" size="100%">Blunt, Emily</style></author><author><style face="normal" font="default" size="100%">RoyChoudhury, Arindam</style></author><author><style face="normal" font="default" size="100%">Butler, Tracy</style></author><author><style face="normal" font="default" size="100%">Dams-O'Connor, Kristen</style></author><author><style face="normal" font="default" size="100%">Khedr, Shahenda</style></author><author><style face="normal" font="default" size="100%">Chen, Chun-Cheng</style></author><author><style face="normal" font="default" size="100%">Shetty, Teena</style></author><author><style face="normal" font="default" size="100%">Winchell, Robert</style></author><author><style face="normal" font="default" size="100%">Hill, N Jeremy</style></author><author><style face="normal" font="default" size="100%">Schiff, Nicholas D</style></author><author><style face="normal" font="default" size="100%">Kuceyeski, Amy</style></author><author><style face="normal" font="default" size="100%">Shah, Sudhin A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparisons of electrophysiological markers of impaired executive attention after traumatic brain injury and in healthy aging.</style></title><secondary-title><style face="normal" font="default" size="100%">Neuroimage</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Neuroimage</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aging</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Injuries</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Injuries, Traumatic</style></keyword><keyword><style  face="normal" font="default" size="100%">Executive Function</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthy Aging</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuropsychological Tests</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">274</style></volume><pages><style face="normal" font="default" size="100%">120126</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Executive attention impairments are a persistent and debilitating consequence of traumatic brain injury (TBI). To make headway towards treating and predicting outcomes following heterogeneous TBI, cognitive impairment specific pathophysiology first needs to be characterized. In a prospective observational study, we measured EEG during the attention network test aimed at detecting alerting, orienting, executive attention and processing speed. The sample (N = 110) of subjects aged 18-86 included those with and without traumatic brain injury: n = 27, complicated mild TBI; n = 5, moderate TBI; n = 10, severe TBI; n = 63, non-brain-injured controls. Subjects with TBI had impairments in processing speed and executive attention. Electrophysiological markers of executive attention processing in the midline frontal regions reveal that, as a group, those with TBI and elderly non-brain-injured controls have reduced responses. We also note that those with TBI and elderly controls have responses that are similar for both low and high-demand trials. In subjects with moderate-severe TBI, reductions in frontal cortical activation and performance profiles are both similar to that of controls who are ∼4 to 7 years older. Our specific observations of frontal response reductions in subjects with TBI and in older adults is consistent with the suggested role of the anterior forebrain mesocircuit as underlying cognitive impairments. Our results provide novel correlative data linking specific pathophysiological mechanisms underlying domain-specific cognitive deficits following TBI and with normal aging. Collectively, our findings provide biomarkers that may serve to track therapeutic interventions and guide development of targeted therapeutics following brain injuries.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McKinnon, Michael L</style></author><author><style face="normal" font="default" size="100%">Hill, N Jeremy</style></author><author><style face="normal" font="default" size="100%">Carp, Jonathan S</style></author><author><style face="normal" font="default" size="100%">Dellenbach, Blair</style></author><author><style face="normal" font="default" size="100%">Thompson, Aiko K</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neural Eng</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neural Eng</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Electric Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Electromyography</style></keyword><keyword><style  face="normal" font="default" size="100%">H-Reflex</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Muscle, Skeletal</style></keyword><keyword><style  face="normal" font="default" size="100%">Peripheral Nerves</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrospective Studies</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Jul 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions' recruitment curves (92 curves across all participants) and on the conditioning protocol's subsequent baseline trials (∼1200 per participant) performed near. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Automatic bounds agreed well with manual estimates: 95% lay within ±2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95% of the automatic estimates of the current required to elicitfell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hill, N Jeremy</style></author><author><style face="normal" font="default" size="100%">Gupta, Disha</style></author><author><style face="normal" font="default" size="100%">Eftekhar, Amir</style></author><author><style face="normal" font="default" size="100%">Brangaccio, Jodi A</style></author><author><style face="normal" font="default" size="100%">Norton, James J S</style></author><author><style face="normal" font="default" size="100%">McLeod, Michelle</style></author><author><style face="normal" font="default" size="100%">Fake, Tim</style></author><author><style face="normal" font="default" size="100%">Wolpaw, Jonathan R</style></author><author><style face="normal" font="default" size="100%">Thompson, Aiko K</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders.</style></title><secondary-title><style face="normal" font="default" size="100%">J Vis Exp</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Vis Exp</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chronic Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Conditioning, Operant</style></keyword><keyword><style  face="normal" font="default" size="100%">Electromyography</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">H-Reflex</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuromuscular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Spinal Cord Injuries</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 08 25</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle responses in people with neuromuscular disorders, which in turn can improve sensorimotor function when applied appropriately. EPOCS monitors the state of specific target muscles-e.g., from surface electromyography (EMG) while standing, or from gait cycle measurements while walking on a treadmill-and automatically triggers calibrated stimulation when pre-defined conditions are met. It provides two forms of feedback that enable a person to learn to modulate the targeted pathway's excitability. First, it continuously monitors ongoing EMG activity in the target muscle, guiding the person to produce a consistent level of activity suitable for conditioning. Second, it provides immediate feedback of the response size following each stimulation and indicates whether it has reached the target value. To illustrate its use, this article describes a protocol through which a person can learn to decrease the size of the Hoffmann reflex-the electrically-elicited analog of the spinal stretch reflex-in the soleus muscle. Down-conditioning this pathway's excitability can improve walking in people with spastic gait due to incomplete spinal cord injury. The article demonstrates how to set up the equipment; how to place stimulating and recording electrodes; and how to use the free software to optimize electrode placement, measure the recruitment curve of direct motor and reflex responses, measure the response without operant conditioning, condition the reflex, and analyze the resulting data. It illustrates how the reflex changes over multiple sessions and how walking improves. It also discusses how the system can be applied to other kinds of evoked responses and to other kinds of stimulation, e.g., motor evoked potentials to transcranial magnetic stimulation; how it can address various clinical problems; and how it can support research studies of sensorimotor function in health and disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">186</style></issue></record></records></xml>