Faculty Bibliography

ABSTRACT/UNASSIGNED:This article discusses the optimal ways to document and code for outpatient evaluation and management (E/M) codes. Since the changes for Current Procedural Terminology (CPT) codes 99202-99215 were finalized for 2021, they have been modified by the Centers for Medicare & Medicaid Services (CMS) in their Medicare Physician Fee Schedule and by technical corrections issued on March 9, 2021. The 21st Century Cures Act mandated that patients can access their notes and test results immediately. These developments have transformed medical documentation and coding for outpatient E/M services. One year in, the authors have a better understanding of the subtleties of documenting and accurately determining levels of service for outpatient encounters using these new rules and regulations, and they share key insights gained by experience with the new system.

Background: Rodent model and in vitro studies suggest brain temperature has the potential to bidirectionally interact with tau pathology in Alzheimer's Disease (AD): tau phosphorylation is robustly increased by small (<1degreeC) reductions in temperature within the human physiological range, and lower brain thermoregulatory areas may be among those first affected by AD pathology. Here, we evaluated the cross-sectional association between body temperature (Tb), as a proxy for brain temperature, and clinically accessible markers of tau pathology in cognitively normal older adults.
Method(s): Tb was measured continuously over 48 hours with ingestible telemetry combined with a novel pre-processing algorithm. This period included 2 nights of nocturnal polysomnography to facilitate delineation of Tb-tau pathology relationships according to waking vs sleeping time intervals. Tau pathology was assessed with both soluble markers including plasma P-tau (P-tau 181) and cerebrospinal fluid (CSF) P-tau, both sampled the following day, and aggregated tau, namely neurofibrillary tangle (NFT) burden in early (I-III) Braak stage areas imaged with MR-PET using the [18F]MK-6240 radio tracer on average ~ one month later Results: Plasma and CSF P-tau levels were highly correlated with one another and with tau tangle radio tracer uptake (NFT burden), p < 0.05 for all comparisons. Lower Tb (quantified by lower mean Tb and a greater proportion of time Tb was under 37.0degreeC) was associated with increased NFT burden and increased plasma and CSF P-tau levels, p < 0.05 all comparisons. For aggregated tau, lower Tb - tau pathology associations were seen during for Tb recorded during waking, but not during sleeping intervals.
Conclusion(s): Preliminary results suggest that lower body temperature in older adults may be associated with increased aggregated and soluble tau pathology

Background: Acute disseminated encephalomyelitis (ADEM) is an inflammatory demyelinating central nervous system (CNS) disorder, characterized by new onset polyfocal neurologic symptoms with encephalopathy and multifocal demyelination, typically occurring in early childhood. The initial diagnosis of ADEM can be challenging as up to 20% of children with multiple sclerosis (MS) or neuromyelitis optica spectrum disorder (NMOSD) are initially diagnosed with ADEM.
Objective(s): To describe characteristics of patients with ADEM vs. recurrent demyelinating syndromes (MS or NMOSD) at the time of initial presentation and identify features at disease onset associated with monophasic demyelinating disease.
Method(s): This is a multicenter observational cohort study of children with a demyelinating disease diagnosis of ADEM, multiphasic ADEM, MS, and NMOSD who were followed at 12 regional pediatric MS referral centers in the US Network of Pediatric MS Centers. Descriptive statistics were used to report patient characteristics, clinical/imaging presenting features and clinical followup outcomes. Logistic regression was used to predict features associated with monophasic demyelination and to identify features associated with poor recovery from ADEM in patients with ADEM-like presentation at 2 years from disease onset.
Result(s): As of July 2019, 872 pediatric patients with a final diagnosis of ADEM (n=89), MS (n= 664) and NMOSD (n=119) were identified. The mean follow-up for all patients was 5.7 +/-3.1 years. ADEM patients were the youngest with mean age at first event 5.4 +/-3.7 years and male predominance (62%), p < 0.001. Severe clinical symptoms at onset were more frequent in ADEM (55% vs. 35% NMOSD and 15% MS, p < 0.001). After 2 years of follow-up, 86.2% of patients initially diagnosed with ADEM retained this diagnosis (ADEM to ADEM), while 10.1% were later reclassified as MS and 3.6% with NMOSD. In univariable regression, younger age at first event and having an antecedent infection at onset were associated with ADEM, while presentation with optic neuritis and gadolinium enhancement on brain MRI were associated with ADEM reclassification to MS or NMOSD after 2 years of follow up. In a multivariable analysis, older age at first event (OR 1.29 [95% CI 1.07-1.56], p = 0.007), presenting with optic neuritis (OR 27.56 [95% CI 3.19-238.14], p = 0.003) and presence of gadolinium enhancement on brain MRI at onset (OR 14.36 [95% CI 2.53-81.36], p = 0.003) were associated with reclassification of ADEM to MS or NMOSD within 2 years. Younger age at onset was associated with higher risk of EDSS 2.0 or higher after 2 years of follow-up (p = 0.0422).
Conclusion(s): Those who remain classified as ADEM vs. those who are reclassified as other demyelinating disorders are younger at onset, more likely to be male, have a more severe initial presentation, and are less likely to have optic neuritis or gadolinium enhancing lesions at onset

Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject's pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.

Introduction: With the corticospinal tract (CST), the corticoreticulospinal tract (CReST) is a major descending motor pathway with widespread bilateral innervation. In animals, CST damage causes a loss of motor control and prompts reorganization in the CReST, possibly with stronger connectivity to arm flexors (e.g. biceps (BIC)) than finger abductors (e.g. first dorsal interosseous (FDI)). CReST reorganization may also contribute to widespread muscle co-activations (i.e. abnormal synergy expression) in the paretic upper extremity (UE). Here, we posited that CReST reorganization after stroke targets the BIC more than the FDI in humans. We predicted that CReST activity, manifesting as abnormal synergy expression, would be more strongly evoked by skilled arm flexion than finger abduction in stroke patients.
Method(s): We studied the paretic UE of 14 chronic stroke patients (F: 8; mean age: 64 (44-85) years; mean post-stroke time: 5 (0.5-14.4) years) and the matched UE of 14 healthy controls (F: 6; mean age: 55 (36-81) years). Subjects used their arm or index finger to move an onscreen cursor through an arc-shaped channel while the remainder of the UE was restrained.We recorded effector kinematics with an infrared camera and electromyographic (EMG) signals from triceps (TRI), deltoid (DLT), BIC, extensor digitorum, flexor carpi radialis (FCR), flexor digitorum superficialis (FDS), and FDI. To quantify movement error, we calculated the average radial distance between the cursor path and the outer channel edge. To quantify abnormal muscle synergies, we applied a non-negative matrix factorization algorithm to the EMG data to identify muscle synergies and calculated the similarity of the synergy vectors between patients and controls; higher similarity scores indicate more normal synergy patterns. We calculated muscle co-activations using correlations between EMG signals of each muscle-pair. We examined group differences with independent t-tests and control-synergy relationships with correlations.
Result(s): Movement errors were higher in patients than controls for the arm (p<0.01) and trended higher for the finger (p=0.074). In the arm, movement errors were inversely related to synergy similarity scores (p<0.01). Higher errors also related to greater FDI-FCR, BIC-TRI, BIC-DLT, and TRI-DLT coactivation (all p<0.05). In the finger, movement errors were unrelated to synergy similarity scores. Lower movement errors related to greater FDSTRI co-activation (p<0.05).
Discussion(s): In the arm, we found that as motor control worsened, the expression of abnormal synergies increased, indicating that CReST activation may increase with loss of CST function. Muscle co-activation was widespread in the UE, in keeping with CReST's multilevel spinal branching. We did not find a relationship between motor control and synergy expression with finger movement, although the long-range co-contraction between the FDS and TRI may speak to a CST-driven stabilizing strategy. Our findings strengthen the notion that CReST reorganization after stroke may preferentially target the arm flexor and its synergies.

Background: The corticoreticulospinal tract (CReST) is a major descending motor pathway in humans, but little is known about its relative innervation of proximal versus distal upper extremity (UE) muscles. In addition, CReST is believed to reorganize after corticospinal injury, but changes in its projections to different paretic muscles remain unknown. Here, we used transcranial magnetic stimulation (TMS) to probe the functional connectivity of the contralesional CReST to an arm muscle (biceps (BIC)) and an intrinsic hand muscle (first dorsal interosseous (FDI)) in healthy and stroke subjects.
Method(s): In this cross-sectional observational study, we examined 15 healthy (F: 7; mean age: 54 (44-81) years; mean UE Fugl-Meyer Assessment (FMA) score: 65 (63-66)) and 16 chronic stroke subjects (F: 10; mean age 62 (44-85) years; mean UE FMA score: 49 (23-64); mean time since stroke: 5 (0.5-14.4) years). We applied TMS to the contralesional hemisphere (assigned in healthy subjects) to elicit ipsilateral motor evoked potentials (iMEPs). We measured contralesional CReST functional connectivity (iMEP presence/absence) and projection strength (iMEP size; mV*ms) to the paretic BIC and FDI. We also measured paretic muscle maximum voluntary contraction and segmental FMA subscores. We examined differences in CReST projections between muscles and subject groups using Fisher's exact tests and general linear mixed models, and examined neurophysiologicalbehavioral relationships with Pearson's and Spearman's correlations.
Result(s): The contralesional CReST made functional connections to both muscles of most subjects (iMEP presence/absence: healthy BIC 14/1, healthy FDI 15/0; stroke BIC 11/5, stroke FDI 15/1). CReST functional connectivity did not differ between muscles in either healthy or stroke subjects (all p>0.172), and did not differ between subject groups for either muscle (all p=1.0). However, CReST projection strength for the muscles diverged between subject groups, manifesting as larger iMEPs in FDIs than BICs in healthy subjects (1.9 mV*ms, p=0.042) and larger iMEPs in BICs than FDIs in stroke subjects (1.0 mV*ms, p=0.042). Muscle iMEP sizes did not significantly differ between healthy and stroke subjects. Muscle strength related to iMEP size in only the paretic BIC of stroke subjects (r(6)=0.853, p=0.007). There was no relationship between FMA subscores and iMEP size for either muscle in either subject group.
Conclusion(s): Our findings indicate that the contralesional CReST has readily identifiable connections to the paretic BIC and FDI. In healthy subjects, the identification of a stronger CReST projection strength to the FDI challenges the notion of a proximal innervation bias by the reticulospinal tract. The shift in projection strength to the BIC after stroke reinforces the concept that the CReST reorganizes after CST injury, with circumscribed behavioral relevance. To confirm a recovery role of the CReST, a longitudinal observation of recovering behavior relating to changing CReST neurophysiology is required.

Introduction: Activities of daily living (ADLs) are performed through a sequence of fundamental units of motion, called primitives. We previously observed that during ADLs, one upper extremity (UE) may engage two objects simultaneously, such as turning on a faucet while holding a toothbrush. These dual-object primitives (DOPs) may demand increased neural resources, as they likely entail the simultaneous execution of two motor plans. Skilled movement by the nondominant healthy UE or the paretic UE has also been found to require increased neural activity. We posited that performance of DOPs would exceed the neural resources available to the nondominant or paretic side, reducing their performance on these sides. We also predicted that the frequency of DOP performance by the paretic UE would relate to its degree of motor impairment.
Method(s): We studied 19 right-hand dominant healthy subjects (10M:9F; 62.0 +/- 13.6 years) and 43 premorbidly right-hand dominant stroke subjects (23M:20F; 24L:19R paretic; 57.5 +/- 14.5 years; 5.7 +/- 6.5 years post stroke). We evaluated subjects on the UE Fugl-Meyer Assessment (FMA) and videotaped their performance of a feeding and toothbrushing task. We analyzed the videos to extract the incidence and count of DOP performance by each UE. To control for dominance and paresis, we normalized DOP counts to the total number of primitives performed by the UE. We used two-tailed Fisher's Exact tests to compare the incidence of DOPs performed by each UE, and Spearman's correlation to examine the relationship between FMA score and DOP frequency.
Result(s): In healthy subjects, the incidence of DOPs was lower on the nondominant than dominant side (12/19 vs. 19/19; p<0.01). In stroke subjects, the incidence of DOPs was lower on the paretic than nonparetic side (19/43 vs. 43/43; p<0.01). The laterality of paresis did not affect whether that UE would perform DOPs (11/19 dominant paretic vs. 8/24 nondominant paretic; p=0.132). In stroke subjects, lower FMA scores were related to a lower frequency of DOP performance on their paretic UE (rho=0.368, p=0.015).
Discussion(s): Our results suggest that UE laterality and impairment may impact DOP performance in healthy and stroke subjects, respectively. DOPs were less commonly performed by the nondominant UE and the paretic UE, and worse impairment was associated with lower DOP performance. We speculate that engaging two objects simultaneously requires additional neural resources that are unavailable to the nondominant or injured motor network. It is conceivable that the return of DOP performance by the paretic UE may track with the availability of a recovered neural substrate.

Introduction: Quantifying upper extremity (UE) motor impairment after stroke is impractical, limiting our ability to tailor rehabilitation training in real time. The current gold-standard measure of impairment, the Fugl-Meyer Assessment (FMA), is time-consuming and requires a trained assessor. The FMA furthermore does not assess functional motions in real-world contexts, which is exactly where we aim our rehabilitation interventions. Here, we took initial steps to develop an approach to automatically quantify UE motor impairment during functional task performance.
Method(s): We studied 51 chronic stroke patients (28F:23M; 57.7 (21.3-84.3) years old; 28L:23R paretic; FMA 43.1 (8-65)).We recorded upper body motion with 9 inertial measurement units (IMUs) while patients performed the FMA and a functional task (moving an object on a horizontal 8-target array). We trained a long short-term memory (LSTM) deep learning model to estimate FMA scores from the recorded motion (training set n=40; test set n=11; 4 LSTM layers with between-layer batch normalization; IMU data windows of 4s with slide of 1s). LSTM-generated impairment scores were computed from FMA motions or from functional motions. To ascertain the accuracy of the approach, we calculated the root mean square error (RMSE) and the Spearman correlation coefficient (rho) between the LSTM scores and the FMA scores from a trained expert. We also examined whether the performance of particular classes of functional primitives (i.e. reach, transport, or reposition) would be sufficient to accurately estimate impairment.
Result(s): Using motions from the FMA performance, our approach estimated FMA scores within 1.1 points of a trained assessor. Using motions from the functional task performance, our approach estimated FMA scores within 1.6 points. Correlation values between the FMA scores and LSTM scores were rho = 0.98 for FMA motions and rho = 0.96 for functional motions. Among the three functional primitives, reaches were the most informative for estimating the impairment scores (RMSE: 1.9 points), followed by transports (RMSE: 2.1 points), and repositions (RMSE: 2.8 points).
Discussion(s): We present a new approach that uses sensor-based motion capture and deep learning to automatically estimate UE motor impairment. This approach has high accuracy and shows high concurrent validity with the FMA, even when it assesses unrelated functional motions. Thus, it may be possible to directly measure impairment from performance of real-world functional tasks, which the FMA does not offer. Estimating impairment during stroke rehabilitation would enable clinicians to tailor treatment strategy in real time.