The Difference Between Expert Dancers' and Non-Dancers Tapping Timing With and Without an Auditory Stimulus at a Slow Tempo.

Our primary purpose in this study was to determine whether trained dancers differed from untrained non-dancers in their ability to accurately control motor timing during finger and heel tapping tasks, both with and without slow isochronous auditory stimuli. Dancers and non-dancers were instructed to synchronize their taps with isochronous auditory stimuli under three conditions: 30, 40, and 50 BPM. After the synchronization phase, participants were asked to continue tapping without the auditory sequences. On the synchronization task, the tapping onset of both groups lagged behind the stimulus onset in all tempo conditions. In all conditions, dancers showed more accurate and stable beat synchronization and continuation than non-dancers. As the tempo condition slowed down (from 50 to 30 BPM), synchronization accuracy decreased while synchronization and continuation variability increased. Unlike for novices, dancers showed no difference between the finger and heel tapping synchronization tasks. During the continuous tasks, their timing accuracy was higher for heel than for finger tapping. Collectively, these findings suggest that dance training, which involves synchronizing bodily movements based on rhythm, may lead to an accumulation of experience that enhances specific sensorimotor skills related to synchronizing movements with external stimuli or continuing rhythmic movements temporally.

Neuroanatomical Signature of the Transition from Normal Cognition to MCI in Parkinson's Disease.

The progression of Parkinson's disease (PD) is often accompanied by cognitive decline. We had previously developed a brain age estimation program utilizing structural MRI data of 949 healthy individuals from publicly available sources. Structural MRI data of 244 PD patients who were cognitively normal at baseline was acquired from the Parkinson Progression Markers Initiative (PPMI). 192 of these showed stable normal cognitive function from baseline out to 5 years (PD-SNC), and the remaining 52 had unstable normal cognition and developed mild cognitive impairment within 5 years (PD-UNC). 105 healthy controls were also included in the analysis as a reference. First, we examined if there were any baseline differences in regional brain structure between PD-UNC and PD-SNC cohorts utilizing the three most widely used atrophy estimation pipelines, i.e., voxel-based morphometry (VBM), deformation-based morphometry and cortical thickness analyses. We then investigated if accelerated brain age estimation with our multivariate regressive machine learning algorithm was different across these groups (HC, PD-SNC, and PD-UNC). As per the VBM analysis, PD-UNC patients demonstrated a noticeable increase in GM volume in the posterior and anterior lobes of the cerebellum, sub-lobar, extra-nuclear, thalamus, and pulvinar regions when compared to PD-SNC at baseline. PD-UNC patients were observed to have significantly older brain age compared to both PD-SNC patients (p=0.009) and healthy controls (p<0.009). The increase in GM volume in the PD-UNC group could potentially indicate an inflammatory or neuronal hypertrophy response, which could serve as a biomarker for future cognitive decline among this population.

Radionuclide Imaging of the Neuroanatomical and Neurochemical Substrate of Cognitive Decline in Parkinson's Disease.

Cognitive impairment is a frequent manifestation of Parkinson's disease (PD), resulting in decrease in patients' quality of life and increased societal and economic burden. However, cognitive decline in PD is highly heterogenous and the mechanisms are poorly understood. Radionuclide imaging techniques like positron emission tomography (PET) and single photon emission computed tomography (SPECT) have been used to investigate the neurochemical and neuroanatomical substrate of cognitive decline in PD. These techniques allow the assessment of different neurotransmitter systems, changes in brain glucose metabolism, proteinopathy, and neuroinflammation in vivo in PD patients. Here, we review current radionuclide imaging research on cognitive deficit in PD with a focus on predicting accelerating cognitive decline. This research could assist in the development of prognostic biomarkers for patient stratification and have utility in the development of ameliorative or disease-modifying therapies targeting cognitive deficit in PD.

Predicting the occurrence of mild cognitive impairment in Parkinson's disease using structural MRI data.

Introduction: Mild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson's disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early anatomical brain changes that precede the transition from healthy cognition to MCI in PD. Methods: Structural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson's Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC, n = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, n = 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier. Results: The average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain. Discussion: Our machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.

High-definition transcranial direct current stimulation desynchronizes refractory status epilepticus.

Recently, we showed that high-definition transcranial direct current stimulation (hd-tDCS) can acutely reduce epileptic spike rates during and after stimulation in refractory status epilepticus (RSE), with a greater likelihood of patient discharge from the intensive care unit compared to historical controls. We investigate whether electroencephalographic (EEG) desynchronization during hd-tDCS can help account for observed anti-epileptic effects. Defining desynchronization as greater power in higher frequencies such as above 30 â€‹Hz ("gamma") and lesser power in frequency bands lower than 30 â€‹Hz, we analyzed 27 EEG sessions from 10 RSE patients who had received 20-minute session(s) of 2-milliamperes of transcranial direct current custom-targeted at the epileptic focus as previously determined by a clinical EEGer monitoring the EEG in real-time. During hd-tDCS, median relative power change over the EEG electrode chains in which power changes were maximal was +4.84%, -5.25%, -1.88%, -1.94%, and +4.99% for respective delta, theta, alpha, beta, and gamma frequency bands in the bipolar longitudinal montage (p â€‹= â€‹0.0001); and +4.13%, -5.44%, -1.81%, -3.23%, and +5.41% in the referential Laplacian montage (p â€‹= â€‹0.0012). After hd-tDCS, median relative power changes reversed over the EEG electrode chains in which power changes were maximal: -2.74%, +4.20%, +1.74%, +1.75%, and -4.68% for the respective delta, theta, alpha, beta, and gamma frequency bands in the bipolar longitudinal montage (p â€‹= â€‹0.0001); and +1.59%, +5.07%, +1.74%, +2.40%, and -5.12% in the referential Laplacian montage (p â€‹= â€‹0.0004). These findings are consistent with EEG desynchronization through theta-alpha-beta-gamma bands during hd-tDCS, helping account for the efficacy of hd-tDCS as an emerging novel anti-epileptic therapy against RSE.

Functional connectivity alterations in PTSD patients with suicidal ideation.

Post-traumatic stress disorder (PTSD) is a highly prevalent psychological disorder characterized by intense feelings of fear or helplessness after experiencing a traumatic event. PTSD is highly comorbid with mood disorders and patients are at increased risk for suicide. The present study aimed to identify neural connectivity alterations associated with suicidal ideation (SI) in PTSD patients by using resting-state functional magnetic resonance imaging. Voxel-to-voxel intrinsic connectivity was compared between PTSD patients with no (N-SI; N = 26) and high (H-SI; N = 7) SI. Region-to-voxel functional connectivity analysis was performed to identify the regions that contributed to intrinsic connectivity changes. H-SI patients had increased connectivity to various brain regions representing the central executive network, salience network, and default mode network in the frontal, temporal, and occipital lobes as well as subcortical structures involved in executive and limbic functioning, and motor systems. These results suggest SI is associated with large network-level alterations in PTSD patients and is not the result of neuronal abnormalities in any one specific area.

Differences in brain aging between sexes in Parkinson's disease.

Parkinson's disease (PD) is linked to faster brain aging. Male sex is associated with higher prevalence, severe symptoms, and a faster progression rate in PD. There remains a significant gap in understanding the function of sex in the process of brain aging in PD. The structural T1-weighted MRI-driven brain-predicted age difference (i.e., Brain-PAD: the actual age subtracted from the brain-predicted age) was computed in a group of 373 people with PD (mean age ± SD: 61.37 ± 9.81, age range: 33-85, 34% female) from the Parkinson's Progression Marker Initiative database using a robust brain-age estimation framework that was trained on 949 healthy subjects. Linear regression models were used to investigate the association between Brain-PAD and clinical variables in PD, stratified by sex. Males with Parkinson's disease (PD-M) exhibited a significantly higher mean Brain-PAD than their female counterparts (PD-F) (t(256) = 2.50, p = 0.012). In the propensity score-matched PD-M group (PD-M*), Brain-PAD was found to be associated with a decline in general cognition, a worse degree of sleep behavior disorder, reduced visuospatial acuity, and caudate atrophy. Conversely, no significant links were observed between these factors and Brain-PAD in the PD-F group. Having 'older' looking brains in PD-M than PD-F supports the idea that sex plays a vital function in PD, such that the PD mechanism may be different in males and females. This study has the potential to broaden our understanding of dissimilarities in brain aging between sexes in the context of PD.

Analysis of Costs for Imaging-Assisted Pharmaceutical Intervention in Alzheimer's Disease with Lecanemab: Snapshot of the First 3 Years.

BACKGROUND: The approval of lecanemab for the treatment of Alzheimer's disease (AD) by the Food and Drug Administration in the United States has sparked controversy over issues of safety, cost, and efficacy. Furthermore, the prognostication of cognitive decline is prohibitively difficult with current methods. The inability to forecast incipient dementia in patients with biological AD suggests a prophylactic scenario wherein all patients with cognitive decline are prescribed anti-AD drugs at the earliest manifestations of dementia; however, most patients with mild cognitive impairment (approximately 77.7%) do not develop dementia over a 3-year period. Prophylactic response therefore constitutes unethical, costly, and unnecessary treatment for these patients. OBJECTIVE: We present a snapshot of the costs associated with the first 3 years of mass availability of anti-AD drugs in a variety of scenarios. METHODS: We consider multiple prognostication scenarios with varying sensitivities and specificities based on neuroimaging studies in patients with mild cognitive impairment to determine approximate costs for the large-scale use of lecanemab. RESULTS: The combination of fluorodeoxyglucose and magnetic resonance was determined to be the most cost-efficient at $177,000 for every positive outcome every 3 years under an assumed adjustment in the price of lecanemab to $9,275 per year. CONCLUSIONS: Imaging-assisted identification of cognitive status in patients with prodromal AD is demonstrated to reduce costs and prevent instances of unnecessary treatment in all cases considered. This highlights the potential of this technology for the ethical prescription of anti-AD medications under a paradigm of imaging-assisted early detection for pharmaceutical intervention in the treatment of AD.

Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospective chart review study, we investigated if epilepsy patients with/without mild cognitive symptoms also show AD-like metabolic patterns determined by the MAD algorithm. Scans from a total of 20 patients with epilepsy were included in this study. Because AD diagnoses are made late in life, only patients aged ≥40 years were considered. For the cognitively impaired patients, four of six were identified as MAD+ (i.e., the FDG-PET image is classified as AD-like by the MAD algorithm), while none of the five cognitively normal patients was identified as MAD+ (χ2 = 8.148, p = 0.017). These results potentially suggest the usability of FDG-PET in prognosticating later dementia development in non-demented epilepsy patients, especially when combined with machine learning algorithms. A longitudinal follow-up study is warranted to assess the effectiveness of this approach.

Hypermetabolic Cerebellar Connectome in Alzheimer's Disease.

Introduction: Regional hypermetabolism in Alzheimer's disease (AD), especially in the cerebellum, has been consistently observed but often neglected as an artefact produced by the commonly used proportional scaling procedure in the statistical parametric mapping. We hypothesize that the hypermetabolic regions are also important in disease pathology in AD. Methods: Using fluorodeoxyglucose (FDG)-positron emission tomography (PET) images from 88 AD subjects and 88 age-sex matched normal controls (NL) from the publicly available Alzheimer's Disease Neuroimaging Initiative database, we developed a general linear model-based classifier that differentiated AD patients from normal individuals (sensitivity = 87.50%, specificity = 82.95%). We constructed region-region group-wise correlation matrices and evaluated differences in network organization by using the graph theory analysis between AD and control subjects. Results: We confirmed that hypermetabolism found in AD is not an artefact by replicating it using white matter as the reference region. The role of the hypermetabolic regions has been further investigated by using the graph theory. The differences in betweenness centrality (BC) between AD and NL network were correlated with region weights of FDG PET-based AD classifier. In particular, the hypermetabolism in cerebellum was accompanied with higher BC. The brain regions with higher BC in AD network showed a progressive increase in FDG uptake over 2 years in prodromal AD patients (n = 39). Discussion: This study suggests that hypermetabolism found in AD may play an important role in forming the AD-related metabolic network. In particular, hypermetabolic cerebellar regions represent a good candidate for further investigation in altered network organization in AD.

A Pilot Study of High-Definition Transcranial Direct Current Stimulation in Refractory Status Epilepticus: The SURESTEP Trial.

Refractory status epilepticus (RSE) is a life-threatening emergency with high mortality and poor functional outcomes in survivors. Treatment is typically limited to intravenous anesthetic infusions and multiple anti-seizure medications. While ongoing seizures can cause permanent neurological damage, medical therapies also pose severe and life-threatening side effects. We tested the feasibility of using high-definition transcranial direct current stimulation (hd-tDCS) in the treatment of RSE. We conducted 20-min hd-tDCS sessions at an outward field orientation, intensity of 2-mA, 4 + 1 channels, and customized for deployment over the electrographic maximum of epileptiform activity ("spikes") determined by real-time clinical EEG monitoring. There were no adverse events from 32 hd-tDCS sessions in 10 RSE patients. Over steady dosing states of infusions and medications in 29 included sessions, median spike rates/patient fell by 50% during hd-tDCS on both automated (p = 0.0069) and human (p = 0.0277) spike counting. Median spike rates for any given stimulation session also fell by 50% during hd-tDCS on automated spike counting (p = 0.0032). Immediately after hd-tDCS, median spike rates/patient remained down by 25% on human spike counting (p = 0.018). Compared to historical controls, hd-tDCS subjects were successfully discharged from the intensive care unit (ICU) 45.8% more often (p = 0.004). When controls were selected using propensity score matching, the discharge rate advantage improved to 55% (p = 0.002). Customized EEG electrode targeting of hd-tDCS is a safe and non-invasive method of hyperacutely reducing epileptiform activity in RSE. Compared to historical controls, there was evidence of a cumulative chronic clinical response with more hd-tDCS subjects discharged from ICU.

Monitoring Alzheimer's Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods.

BACKGROUND: We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects. OBJECTIVE: We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD. METHODS: MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior. RESULTS: The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01). CONCLUSION: These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD.

Predicting cognitive decline in Parkinson's disease using FDG-PET-based supervised learning.

BackgroundCognitive impairment is a common symptom of Parkinson's disease (PD) that increases in risk and severity as the disease progresses. An accurate prediction of the risk of progression from the mild cognitive impairment (MCI) stage to the dementia (PDD) stage is an unmet clinical need.MethodsWe investigated the use of a supervised learning algorithm called the support vector machine (SVM) to retrospectively stratify patients on the basis of brain fluorodeoxyglucose-PET (FDG-PET) scans. Of 43 patients with PD-MCI according to the baseline scan, 23 progressed to PDD within a 5-year period, whereas 20 maintained stable MCI. The baseline scans were used to train a model, which separated patients identified as PDD converters versus those with stable MCI with 95% sensitivity and 91% specificity.ResultsIn an independent validation data set of 19 patients, the AUC was 0.73, with 67% sensitivity and 80% specificity. The SVM model was topographically characterized by hypometabolism in the temporal and parietal lobes and hypermetabolism in the anterior cingulum and putamen and the insular, mesiotemporal, and postcentral gyri. The performance of the SVM model was further tested on 2 additional data sets, which confirmed that the model was also sensitive to later-stage PDD (17 of 19 patients; 89% sensitivity) and dementia with Lewy bodies (DLB) (16 of 17 patients; 94% sensitivity), but not to normal cognition PD (2 of 17 patients). Finally, anti-PD medication status did not change the SVM classification of the other set of 10 patients with PD who were scanned twice, ON and OFF medication.ConclusionsThese results potentially indicate that the proposed FDG-PET-based SVM classifier has utility for providing an accurate prognosis of dementia development in patients with PD-MCI.

Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder.

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.

Age-related reduction of hemispheric asymmetry by pigeons: A behavioral and FDG-PET imaging investigation of visual discrimination.

Pigeons are long-lived and slowly aging animals that present a distinct opportunity to further our understanding of age-related brain changes. Generally, for pigeons, the left hemisphere contributes to discrimination of local information, whereas the right contributes to processing of global information. The function of each hemisphere may be examined by covering one eye, as the optic nerves decussate almost completely in birds, directing the majority of visual information to the contralateral hemisphere. Using this eye-capping technique, we investigated pigeons' ability to select grains from among grit while under binocular and monocular viewing conditions, across three different age groups. Prior to the grit-grain discrimination task, pigeons were injected with a radioactive tracer, which was taken up by the brain as the pigeons performed the task. Upon completion of the discrimination task, the pigeons' brains were imaged via [18F] fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This process allowed us to compare hemispheric activity during the discrimination task for each individual within each age group. The Very Old subjects showed significantly worse discrimination performance compared to the Adult and Old subjects, particularly when needing to search primarily with their right hemisphere. Furthermore, the Very Old subjects did not show differences in hemispheric activation when performing the task, whereas the left hemisphere was most active for the Adult and Old groups. To our knowledge, this is the first study to use FDG-PET imaging to evaluate whether the pigeon brain shows evidence of age-related reduction in hemispheric asymmetry during a visual discrimination task.

Metabolic imaging and plasticity.

Positron emission tomography greatly advanced our understanding on the underlying neural mechanisms of movement disorders. PET with flurodeoxyglucose (FDG) is especially useful as it depicts regional metabolic activity level that can predict patients' symptoms. Multivariate pattern analysis has been used to determine and quantify the co-varying brain networks associated with specific clinical traits of neurodegenerative disease. The result is a biomarker, useful for diagnosis, treatments, and follow up studies. Parkinsonian traits and parkinsonisms are associated with specific spatial pattern of metabolic abnormality useful for differential diagnosis. This approach has also been used for monitoring disease progression and novel treatment responses mostly in Parkinson's disease. In this book chapter, we, illustrate and discuss the significance of the brain networks associated with disease and their modification with neuroplastic changes.

Alzheimer's Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases.

Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis in the early stages of dementia remains challenging. Fueled by AD Neuroimaging Initiatives (ADNI) (Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.), a number of neuroimaging biomarkers for AD have been proposed, yet it remains to be seen whether these markers are also sensitive to other types of dementia. We assessed AD-related metabolic patterns in 27 patients with diverse forms of dementia (five had probable/possible AD while others had atypical cases) and 20 non-demented individuals. All participants had positron emission tomography (PET) scans on file. We used a pre-trained machine learning-based AD designation (MAD) framework to investigate the AD-related metabolic pattern among the participants under study. The MAD algorithm showed a sensitivity of 0.67 and specificity of 0.90 for distinguishing dementia patients from non-dementia participants. A total of 18/27 dementia patients and 2/20 non-dementia patients were identified as having AD-like patterns of metabolism. These results highlight that many underlying causes of dementia have similar hypometabolic pattern as AD and this similarity is an interesting avenue for future research.

Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury.

Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the "one-treatment fits all" approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex "-omics" data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.

Computerized Dual-Task Testing of Gait Visuomotor and Cognitive Functions in Parkinson's Disease: Test-Retest Reliability and Validity.

BACKGROUND: Mobility and cognitive impairments in Parkinson's disease (PD) often coexist and are prognostic of adverse health events. Consequently, assessment and training that simultaneously address both gait function and cognition are important to consider in rehabilitation and promotion of healthy aging. For this purpose, a computer game-based rehabilitation treadmill platform (GRP) was developed for dual-task (DT) assessment and training. OBJECTIVE: The first objective was to establish the test-retest reliability of the GRP assessment protocol for DT gait, visuomotor and executive cognitive function in PD patients. The second objective was to examine the effect of task condition [single task (ST) vs. DT] and disease severity (stage 2 vs. stage 3) on gait, visuomotor and cognitive function. METHODS: Thirty individuals aged 55 to 70 years, diagnosed with PD; 15 each at Hoehn and Yahr scale stage 2 (PD-2) and 3 (PD-3) performed a series of computerized visuomotor and cognitive game tasks while sitting (ST) and during treadmill walking (DT). A treadmill instrumented with a pressure mat was used to record center of foot pressure and compute the average and coefficient of variation (COV) of step time, step length, and drift during 1-min, speed-controlled intervals. Visuomotor and cognitive game performance measures were quantified using custom software. Testing was conducted on two occasions, 1 week apart. RESULTS: With few exceptions, the assessment protocol showed moderate to high intraclass correlation coefficient (ICC) values under both ST and DT conditions for the spatio-temporal gait measures (average and COV), as well as the visuomotor tracking and cognitive game performance measures. A significant decline in gait, visuomotor, and cognitive game performance measures was observed during DT compared to ST conditions, and in the PD-3 compared to PD-2 groups. CONCLUSION: The high to moderate ICC values along with the lack of systematic errors in the measures indicate that this tool has the ability to repeatedly record reliable DT interference (DTI) effects over time. The use of interactive digital media provides a flexible method to produce and evaluate DTI for a wide range of executive cognitive activities. This also proves to be a sensitive tool for tracking disease progression. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, identifier NCT03232996.

Changes in Metabolic Activity and Gait Function by Dual-Task Cognitive Game-Based Treadmill System in Parkinson's Disease: Protocol of a Randomized Controlled Trial.

Balance and gait impairments, and consequently, mobility restrictions and falls are common in Parkinson's disease (PD). Various cognitive deficits are also common in PD and are associated with increased fall risk. These mobility and cognitive deficits are limiting factors in a person's health, ability to perform activities of daily living, and overall quality of life. Community ambulation involves many dual-task (DT) conditions that require processing of several cognitive tasks while managing or reacting to sudden or unexpected balance challenges. DT training programs that can simultaneously target balance, gait, visuomotor, and cognitive functions are important to consider in rehabilitation and promotion of healthy active lives. In the proposed multi-center, randomized controlled trial (RCT), novel behavioral positron emission tomography (PET) brain imaging methods are used to evaluate the molecular basis and neural underpinnings of: (a) the decline of mobility function in PD, specifically, balance, gait, visuomotor, and cognitive function, and (b) the effects of an engaging, game-based DT treadmill walking program on mobility and cognitive functions. Both the interactive cognitive game tasks and treadmill walking require continuous visual attention, and share spatial processing functions, notably to minimize any balance disturbance or gait deviation/stumble. The ability to "walk and talk" normally includes activation of specific regions of the prefrontal cortex (PFC) and the basal ganglia (site of degeneration in PD). The PET imaging analysis and comparison with healthy age-matched controls will allow us to identify areas of abnormal, reduced activity levels, as well as areas of excessive activity (increased attentional resources) during DT-walking. We will then be able to identify areas of brain plasticity associated with improvements in mobility functions (balance, gait, and cognition) after intervention. We expect the gait-cognitive training effect to involve re-organization of PFC activity among other, yet to be identified brain regions. The DT mobility-training platform and behavioral PET brain imaging methods are directly applicable to other diseases that affect gait and cognition, e.g., cognitive vascular impairment, Alzheimer's disease, as well as in aging.