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In the modern era, inclination towards a more conservative approach is being advocated and this has become possible through various innovative machinery and techniques. A conservative tooth preparation helps in retaining more of a tooth structure which would determine the prognosis of the treatment and the fate of the tooth in the long run. Even though we have a high focus on the conservative approach, we should keep in mind that there is always a chance of errors which is caused due to inadequate access to the canals. It is ultimately the clinicians call to decide between the benefit to the risk ratio and also its feasibility, all of which should result in good prognosis as well as a fruitful qualitative treatment outcome for the patient. Keywords : Conservation,Endodontics,CBCT,Prognosis,Clinician,Treatment

Functional resonance magnetic imaging (fMRI) allows for identification of eloquent cortex in pre-treatment planning. Previous studies have shown a correlation among lesion to activation distance (LAD) measures and morbidity and mortality. This study investigates the relationship between LAD, well-established language centers (Wernicke’s and Broca’s), and language performance measures. We included a sample population of brain tumor patients that received language fMRI (verbal fluency and sentence verification) for pre-treatment assessment (n = 51). LAD to the nearest language area was measured and divided into groups ≤ 10 mm and > 10 mm. Verbal fluency scores were compared between these groups. Additionally, patients were divided into similar groups based on LAD to either Broca’s or Wernicke’s areas, and the verbal fluency scores and sentence verification accuracy (n = 29) were subsequently compared between groups. Brain tumor patients with LAD ≤ 10 mm to either language area had significantly lower verbal fluency scores (p = 0.028). The difference in verbal fluency scores between groups with LAD ≤ 10 mm and > 10 mm to Wernicke’s area trends toward significance (p = 0.067). The sentence verification accuracy was significantly lower in patients with LAD ≤ 10 mm to either language area (p = 0.039). These findings suggest that there exists a significant relationship between LAD to language centers and measures; greater language deficits are seen when LAD ≤ 10 mm.

Throughout adulthood, the brain undergoes an array of structural and functional changes during the typical aging process. These changes involve decreased brain volume, reduced synaptic density, and alterations in white matter (WM). Although there have been some previous neuroimaging studies that have measured the ability of adult language production and its correlations to brain function, structural gray matter volume, and functional differences between young and old adults, the structural role of WM in adult language production in individuals across the life span remains to be thoroughly elucidated. This study selected 38 young adults and 35 old adults for diffusion tensor imaging (DTI) and performed the Controlled Oral Word Association Test to assess verbal fluency (VF). Tract-Based Spatial Statistics were employed to evaluate the voxel-based group differences of diffusion metrics for the values of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and local diffusion homogeneity (LDH) in 12 WM regions of interest associated with language production. To investigate group differences on each DTI metric, an analysis of covariance (ANCOVA) controlling for sex and education level was performed, and the statistical threshold was considered at p < 0.00083 (0.05/60 labels) after Bonferroni correction for multiple comparisons. Significant differences in DTI metrics identified in the ANCOVA were used to perform correlation analyses with VF scores. Compared to the old adults, the young adults had significantly (1) increased FA values on the bilateral anterior corona radiata (ACR); (2) decreased MD values on the right ACR, but increased MD on the left uncinate fasciculus (UF); and (3) decreased RD on the bilateral ACR. There were no significant differences between the groups for AD or LDH. Moreover, the old adults had only a significant correlation between the VF score and the MD on the left UF. There were no significant correlations between VF score and DTI metrics in the young adults. This study adds to the growing body of research that WM areas involved in language production are sensitive to aging.

Stroke is a leading cause of acquired long-term upper extremity motor disability. Current standard of care trajectories fail to deliver sufficient motor rehabilitation to stroke survivors. Recent research suggests that use of brain-computer interface (BCI) devices improves motor function in stroke survivors, regardless of stroke severity and chronicity, and may induce and/or facilitate neuroplastic changes associated with motor rehabilitation. The present sub analyses of ongoing crossover-controlled trial NCT02098265 examine first whether, during movements of the affected hand compared to rest, ipsilesional Mu rhythm desynchronization of cerebral cortical sensorimotor areas [Brodmann’s areas (BA) 1-7] is localized and tracks with changes in grip force strength. Secondly, we test the hypothesis that BCI intervention results in changes in frequency-specific directional flow of information transmission (direct path functional connectivity) in BA 1-7 by measuring changes in isolated effective coherence (iCoh) between cerebral cortical sensorimotor areas thought to relate to electrophysiological signatures of motor actions and motor learning. A sample of 16 stroke survivors with right hemisphere lesions (left hand motor impairment), received a maximum of 18–30 h of BCI intervention. Electroencephalograms were recorded during intervention sessions while outcome measures of motor function and capacity were assessed at baseline and completion of intervention. Greater desynchronization of Mu rhythm, during movements of the impaired hand compared to rest, were primarily localized to ipsilesional sensorimotor cortices (BA 1-7). In addition, increased Mu desynchronization in the ipsilesional primary motor cortex, Post vs. Pre BCI intervention, correlated significantly with improvements in hand function as assessed by grip force measurements. Moreover, the results show a significant change in the direction of causal information flow, as measured by iCoh, toward the ipsilesional motor (BA 4) and ipsilesional premotor cortices (BA 6) during BCI intervention. Significant iCoh increases from ipsilesional BA 4 to ipsilesional BA 6 were observed in both Mu [8–12 Hz] and Beta [18–26 Hz] frequency ranges. In summary, the present results are indicative of improvements in motor capacity and behavior, and they are consistent with the view that BCI-FES intervention improves functional motor capacity of the ipsilesional hemisphere and the impaired hand.

While most survivors of stroke experience some spontaneous recovery and receive treatment in the subacute setting, they are often left with persistent impairments in upper limb sensorimotor function which impact autonomy in daily life. Brain-Computer Interface (BCI) technology has shown promise as a form of rehabilitation that can facilitate motor recovery after stroke, however, we have a limited understanding of the changes in functional connectivity and behavioral outcomes associated with its use. Here, we investigate the effects of EEG-based BCI intervention with functional electrical stimulation (FES) on resting-state functional connectivity (rsFC) and motor outcomes in stroke recovery. 23 patients post-stroke with upper limb motor impairment completed BCI intervention with FES. Resting-state functional magnetic resonance imaging (rs-fMRI) scans and behavioral data were collected prior to intervention, post- and 1-month post-intervention. Changes in rsFC within the motor network and behavioral measures were investigated to identify brain-behavior correlations. At the group-level, there were significant increases in interhemispheric and network rsFC in the motor network after BCI intervention, and patients significantly improved on the Action Research Arm Test (ARAT) and SIS domains. Notably, changes in interhemispheric rsFC from pre- to both post- and 1 month post-intervention correlated with behavioral improvements across several motor-related domains. These findings suggest that BCI intervention with FES can facilitate interhemispheric connectivity changes and upper limb motor recovery in patients after stroke.

BACKGROUND AND PURPOSE: In this pilot study, we investigated functional brain activation changes in patients with Crohn’s disease (CD) in remission compared to age and gender matched healthy controls (HCs). METHODS: Data from 20 patients with CD in remission (age range 19 to 63 years) and 20 HCs (matched in age and gender) were analyzed. Task fMRI data was collected while participants performed a cognitive (phonemic verbal fluency) task in the scanner. All participants also performed the same task outside the scanner. RESULTS: Task fMRI results showed greater bi-hemispheric activation in CD patients compared to controls. Because this pattern is commonly reported with normal aging, we performed further analyses to investigate fMRI responses in a sub-set of the younger CD patients (N=12, age < = 35 years) compared to matched young HCs (age < = 35 years), and an older cohort of HCs (age > =50 years). Results showed that task activation patterns were similar between young CD patients and older HCs, and that both groups differed significantly from younger HCs. Activation intensity in specific brain regions for patients was associated with disease duration. CONCLUSIONS: These results suggest that Crohn’s disease patients in remission may show accelerated signs of aging in terms of brain responses to a typical cognitive task. Future work with larger sample size will need to replicate these results as well as investigate influence of factors such as chronicity of the disease and medication effects on task associated brain activation patterns in this patient population.

Previous studies have found neural alterations in regions involved in cognitive and affective functions among Crohn’s disease (CD) patients. The present work recruited 18 CD patients and 18 age-gender matched healthy controls (HC) and specifically compared differences in resting-state functional connectivity (RSFC) within the executive control network (ECN) which has been implicated in cognitive function and default mode network (DMN), which has been implicated in affective function. Additionally, we examined the correlations between RSFC in ECN and verbal fluency (VF) in both groups as well as RSFC in DMN and anxiety level in the CD group. Results showed significantly increased RSFC between the right middle frontal gyrus and right inferior parietal lobule in ECN, as well as increased RSFC between the right precuneus and right posterior cingulate cortex in DMN, among CD patients compared to HC. However, the correlations between ECN/DMN and behavioral scores in each group were not significant, which was possibility due to the limited sample size. These findings suggest that CD patients may experience changes in the connectivity patterns in ECN and DMN. Increased connectivity observed on these networks could be a potential biomarker of a neuropsychiatric manifestation of CD.

To study glycemic control, mortality and long-term complications in children with type 1 diabetes (T1D).Cross-sectional study.Referral centre at a government teaching hospital.Patients with T1D with age £18 years at onset.We retrospectively collected demographic data from computer records from 1991 to 2015. Prospective study for outcomes was conducted between 2012 and 2016.Mortality rate, glycosylated hemoglobin (HbA1c), and microvascular complication rate.The proportion of T1D patients (n=512)5 years of age at onset was 18.6% between 1995 and 2004, and 24.2% in 2005-2014 (P0.001). Twenty eight patients had died out of 334 whose living status was known (mortality 1.1 per 100 patient-years over 2549 patient-years follow up). Median (range) HbA1c (n=257) was 8.3% (5.1-15.0%). At least one episode of severe hypoglycemia (coma/seizure/inability to assist self) had occurred in 22.8% patients over two years. Hypertension was present in 11.7% patients. Microvascular complications screen in 164 eligible patients [median (range) age 20 (8-45) y and duration of diabetes 9.1 (5-30) y] showed diabetic nephropathy in 3.0%, proliferative retinopathy in 3.6% and LDL cholesterol100 mg/dL in 34% patients.The mortality rate and prevalence of hypertension were high, given the short duration of diabetes of the patients. The proportion of patients with age ≤5 years at onset of diabetes has increased at our center.

Transgendersin India are highly vulnerable to stigma, violence and discrimination in all the fields of life including in the health care settings. Transgenders health needs in India, Health services and schemes for transgender welfare available in the national level and services offered for transgenders in Kerala are discussed in this article. This article was compiled through extensive literature search, consultations with a small group of transgender persons across Kerala, both trans men and trans women, and also with personnel associated with TG Cell and NGOs working in the field of transgender health care in Kerala

BACKGROUND: It is unclear how preoperative neurodegeneration and postoperative changes in EEG delta power relate to postoperative delirium severity. We sought to understand the relative relationships between neurodegeneration and delta power as predictors of delirium severity. METHODS: We undertook a prospective cohort study of high-risk surgical patients (>65 yr old) to identify predictors of peak delirium severity (Delirium Rating Scale-98) with twice-daily delirium assessments (NCT 03124303). Participants (n=86) underwent preoperative MRI; 54 had both an MRI and a postoperative EEG. Cortical thickness was calculated from the MRI and delta power from the EEG. RESULTS: In a linear regression model, the interaction between delirium status and preoperative mean cortical thickness (suggesting neurodegeneration) across the entire cortex was a significant predictor of delirium severity (P

Objective: This study is part of a clinical trial designed to test the efficacy of an EEG-based BCI intervention for upper extremity motor rehabilitation in stroke survivors. The analyses presented here focus on the effectiveness of BCI intervention as a function of somatosensory integrity. Introduction: Human motor control requires integration of sensory and motor signals in the generation of motor commands. Stroke lesions often result in sensorimotor impairments and survivors may require rehabilitation to regain motor function and capacity. Stroke affects individuals differentially, based on a range of factors, including, but not limited to, lesion location and volume. Such factors may restrain recovery potential. Hypothesis: We tested the hypothesis that stroke survivors with measurable somatosensory impairments realize the same amount of motor recovery as those participants without somatosensory impairments. Methods: N= 23 stroke survivors participated in up to 30 hours of BCI intervention (13.8 ±1.3, mean + SD) for upper extremity rehabilitation, as measured by the ARAT. Participants were grouped post-hoc on presence or absence of somatosensory impairments, as measured by the NIHSS subdomains of Sensory (i.e. cutaneous), and Motor Arm (i.e. proprioceptive) and their group means compared. Results: The hypothesis was not confirmed. Mean ARAT scores at completion and follow up differed between groups (Cutaneous loss: ARAT mean change at completion: 0.9 ± 2.23, p= 0.234; ARAT mean change at follow-up: 1.20 ± 2.860, p = 0.217), (No Cutaneous loss: ARAT mean change at completion: 2.15 ± 6.34, p= 0.244; ARAT mean change at follow-up: 4.39 ± 6.41, p= 0.0297) (Proprioceptive loss: ARAT mean change at completion: 0.867 ± 3.66, p= 0.375, ARAT mean change at follow-up: 2.47 ± 5.38, p= 0.097), (No Proprioceptive loss: ARAT mean change at completion: 3 ± 6.80, p= 0.252, ARAT mean change at follow-up: 4 ± 5.42, p= 0.075). Conclusions: These results suggest that BCI intervention is more effective at delivering motor improvements in participants with less somatosensory impairments. These results are consistent with the view that somatosensory system integrity may be key to BCI motor rehabilitation of brain injuries following stroke.

Congenital Zika virus (ZIKV) exposure results in a spectrum of disease ranging from severe birth defects to delayed onset neurodevelopmental deficits. ZIKV-related neuropathogenesis, predictors of birth defects, and neurodevelopmental deficits are not well defined in people. Here we assess the methodological and statistical feasibility of a congenital ZIKV exposure macaque model for identifying infant neurobehavior and brain abnormalities that may underlie neurodevelopmental deficits. We inoculated five pregnant macaques with ZIKV and mock-inoculated one macaque in the first trimester. Following birth, growth, ocular structure/function, brain structure, hearing, histopathology, and neurobehavior were quantitatively assessed during the first week of life. We identified the typical pregnancy outcomes of congenital ZIKV infection, with fetal demise and placental abnormalities. We estimated sample sizes needed to define differences between groups and demonstrated that future studies quantifying brain region volumes, retinal structure, hearing, and visual pathway function require a sample size of 14 animals per group (14 ZIKV, 14 control) to detect statistically significant differences in at least half of the infant exam parameters. Establishing the parameters for future studies of neurodevelopmental outcomes following congenital ZIKV exposure in macaques is essential for robust and rigorous experimental design.

Background: Functional Magnetic Resonance Imaging (fMRI) is a presurgical planning technique used to localize functional cortex so as to maximize resection of diseased tissue and avoid viable tissue. In this retrospective study, we examined differences in morbidity and mortality of brain tumor patients who received preoperative fMRI in comparison to those who did not. Methods: Brain tumor patients (n = 206) were selected from a retrospective review of neurosurgical case logs from 2001 to 2009 at the University of Wisconsin-Madison. Results: Univariate analysis showed improved mortality in the fMRI group and the fMRI + Electrical Cortical Stimulation Mapping (ECM) group compared to the No-fMRI group. Multivariate analyses showed improved mortality of the fMRI group and the fMRI + ECM group compared to the No-fMRI group, with age and tumor grade being the most significant influencers. Overall, the fMRI group showed survival benefits at 3 years; twice that of the No-fMRI group. Furthermore, patients with high-grade tumors showed significant survival benefits in the fMRI group, while patients with low-grade tumors did not (controlling for age and ECM). There was also a significant difference in the two groups with respect to morbidity, with patients receiving fMRI showing improved outcomes in the motor and language domains. Conclusions: This study analyzing a large retrospective series of brain tumor patients with and without the use of fMRI in the preoperative planning has resulted in improved mortality and morbidity outcomes with the use of fMRI. These results point to the importance of incorporating fMRI in presurgical planning in the clinical management of patients with brain tumors. Keywords: Brain mapping, Brain tumors, Functional neuroimaging

ObjectivesCervical cancer is the second most common cancer in women in developing countries, including India. Recently, microRNAs (miRNAs) are gaining importance in cancer biology because of their involvement in various cellular processes. The present study aimed to profile miRNA expression pattern in cervical cancer, identify their target genes, and understand their role in carcinogenesis.MethodsHuman papillomavirus (HPV) infection statuses in samples were assessed by heminested polymerase chain reaction followed by direct DNA sequencing. Next-generation sequencing and miRNA microarray were used for miRNA profiling in cervical cancer cell lines and tissue samples, respectively. MicroRNA signature was validated by quantitative real-time PCR, and biological significance was elucidated using various in silico analyses.ResultsCervical cancer tissues samples were mostly infected by HPV type 16 (93%). MicroRNA profiling showed that the pattern of miRNA expression differed with respect to HPV positivity in cervical cancer cell lines. However, target and pathway analyses indicated identical involvement of these significantly deregulated miRNAs in HPV-positive cervical cancer cell lines irrespective of type of HPV infected. Microarray profiling identified a set of miRNAs that are differentially deregulated in cervical cancer tissue samples which were validated using quantitative real-time PCR. In silico analyses revealed that the signature miRNAs are mainly involved in PI3K-Akt and mTOR pathways.ConclusionsThe study identified that high-risk HPV induces similar carcinogenic mechanism irrespective of HPV type. The miRNA signature of cervical cancer and their target genes were also elucidated, thereby providing a better insight into the molecular mechanism underlying cervical cancer development.

Natural Language Processing NLP is the one of the major filed of Natural Language Generation NLG . NLG can generate natural language from a machine representation. Generating suggestions for a sentence especially for Indian languages is much difficult. One of the major reason is that it is morphologically rich and the format is just reverse of English language. By using deep learning approach with the help of Long Short Term Memory LSTM layers we can generate a possible set of solutions for erroneous part in a sentence. To effectively generate a bunch of sentences having equivalent meaning as the original sentence using Deep Learning DL approach is to train a model on this task, e.g. we need thousands of examples of inputs and outputs with which to train a model. Veena S Nair | Amina Beevi A "Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23842.pdf

Patients with inflammatory bowel disease have been shown to have abnormal brain morphometry or function, which are associated with psychological symptoms such as stress, depression or anxiety. The present work recruited 20 Crohn’s disease patients in remission (CDs) and 20 age-gender-handedness-education matched healthy controls (HCs) and compared their brain white matter microstructural properties using Diffusion Tensor Imaging (DTI). Additionally, we examined the correlations between the microstructural properties and cognition (verbal fluency language task, VF) and affect (anxiety) in both groups as well as disease duration in CDs. Results showed that CDs exhibited significant alterations in microstructural properties compared to HCs in various white matter tracts relevant to language function despite no significant difference in VF scores. Furthermore, CDs’ microstructural changes exhibited correlations with anxiety level and disease duration. These findings suggest that CD patients may experience changes in white matter microstructural properties which may be a biomarker of neuropsychiatric comorbidities of CD.

This research seeks to identify source localization of neuromechanical changes associated with gains in hand grip function following BCI intervention sessions for upper extremity motor recovery. Mu rhythm desynchronization (ERD) of contralateral brain motor areas is thought to occur during movement and changes in brain motor area ERD relate to changes in behavioral measures, hand grip function, with BCI intervention. N=16 right hemisphere survivors participated in 9-15 sessions with BCI. Participants executed hand movements in response to visual cues on a computer screen with the corresponding audio instructions (e.g., Left, Right, Rest). The sessions (i.e. pre and post BCI intervention) contained two runs, each consisting of 15 trials for rest, left hand, and right hand movements (i.e., 5 trials for each of the three conditions) presented in random order. Data from the pre and post conditions were then grand averaged. sLORETA estimates were calculated as follows: 1) Clean EEG data segmented (LEFT hand attempted movements and REST) separately for PRE and POST conditions. 2) Cross-spectra (MU[8-12 Hz]) were computed then averaged (i.e. 1 average per each subject separately for left, right and rest trials. 3) sLORETA of MU power estimates at 6239 cortical locations/voxels were obtained, normalized across subjects. In the sLORETA implementation, computations made in realistic head model using the MNI152 template, with 3D solution space restricted to cortical gray matter, as determined by the probabilistic Talairach atlas. The specific frequency band cross-spectra (frequency-domain) obtained from the average-reference potential data, were the inputs for source localization. 4) sLORETA estimates between the 2 conditions [Movement - Rest] were then calculated in cortical space. Pearson correlation of voxel-wise Mu ERD with the change in Hand Grip were calculated. A stronger Mu ERD in the ipsilesional primary motor cortical area (BA 4) at ‘post’ intervention (i.e., more negative values compared to ‘pre’) was associated with a greater change in hand grip (r=-0.435, p=0.046 [1-tailed]). Increases in functional recovery of the impaired upper extremity (Hand Grip) have a direct correlation with greater changes in Mu ERD of ipsilesional motor brain areas.

Objective: This research seeks to identify changes in frequency specific directional flow of information transmission (functional connectivity) as a result of BCI intervention. Direct path functional connectivity (isolated effective coherence (iCOH)) changes between motor-related brain areas (Brodmann areas (BA) 1-7) are thought to relate to electrophysiological signatures of motor learning. Methods: N=16 right hemisphere stroke survivors participated in 9-15 sessions with the BCI. Participants executed hand movements prompted by visual cues on a monitor concordantly with the corresponding audio instructions (e.g., Left, Right, Rest). The ‘screening’ sessions (i.e. pre and post BCI intervention) contained two runs, each consisting of 15 trials for rest, left hand, and right hand movements (i.e., 5 trials for each of the three conditions, the order of trials in a run was random). iCOH is based on formulating a multivariate autoregressive model from time series measurements, and calculating the corresponding partial coherences after setting all irrelevant connections to zero, according to Pascual-Marqui et al. 2014. From the spectral density matrix (including Mu[8-12 Hz] and Beta[18-26 Hz] ranges) obtained from estimated signals in the selected ROI, the partial coherences between any pair of nodes can be calculated. The t-statistics was performed for iCOH values between post and pre of the impaired (left) hand movement trials and thresholded at p=0.05( t=2.13, uncorrected). Results: In the Mu band, iCOH decreased pre to post from contralesional BA 4 to ipsilesional BA 5 and increased from ipsilesional BA 4 to ipsilesional BA 6. In Beta band, iCOH decreased pre to post from ipsilesional BA 5 to contralesional BA 7 and increased from ipsilesional BA 4 and & contralesional BA 7 to ipsilesional BA 6 and also from ipsilesional BA 1,2,3 to contralesional BA 4. Conclusion: There is a consistent change in the direction of information flow, as measured by iCOH, toward the ipsilesional motor and pre-motor areas (BA 4,6). Significant iCOH increases are observed in both Mu and Beta from ipsilesional BA 4 to ipsilesional BA 6 suggesting an increase in participation of motor brain areas associated with motor planning and execution with participation in BCI intervention.

Functional magnetic resonance imaging (fMRI)-based functional connectivity (FC) commonly characterizes the functional connections in the brain. Conventional quantification of FC by Pearson's correlation captures linear, time-domain dependencies among blood-oxygen-level-dependent (BOLD) signals. We examined measures to quantify FC by investigating: (i) Is Pearson's correlation sufficient to characterize FC? (ii) Can alternative measures better quantify FC? (iii) What are the implications of using alternative FC measures? FMRI analysis in healthy adult population suggested that: (i) Pearson's correlation cannot comprehensively capture BOLD inter-dependencies. (ii) Eight alternative FC measures were similarly consistent between task and resting-state fMRI, improved age-based classification and provided better association with behavioral outcomes. (iii) Formulated hypotheses were: first, in lieu of Pearson’s correlation, an augmented, composite and multi-metric definition of FC is more appropriate; second, canonical large-scale brain networks may depend on the chosen FC measure. A thorough notion of FC promises better understanding of variations within a given population.

Cortical thickness is traditionally derived from T1-weighted MRI images. Recent studies have shown an improvement in segmentation with the combination of T1 + T2-FLAIR images. MRI data from 54 adults (mean: 71 years, 65–81 years, 48% females) that are part of an ongoing cohort study were analyzed to investigate whether T1 + T2-FLAIR cortical thickness measurements were superior to those derived from T1-weighted images in identifying age-related atrophy. T1-weighted and T2-FLAIR MRI images were processed through FreeSurfer v6.0. Data was extracted using the Desikan-Killiany (DKT) atlas. FreeSurfer’s GUI QDEC examined age-related atrophy. Nonparametric tests, effect sizes, and Pearson correlations examined differences between T1-only and T1 + T2-FLAIR cortical thickness data. These analyses demonstrated that T1 + T2-FLAIR processed images significantly improved the segmentation of gray matter (chi-square x2, p

Highlights • TLE patients have older brain ages both functionally and structurally. • Functional brain age is a better predictor of cognitive abilites in TLE patients. • Weaker bilateral frontal/temporal connections affect functional brain aging in TLE. • Functional age mediates some of chronological age-fluid cognition relationships. • Accelerated brain aging in TLE affected by both seizure burden and polytherapy., The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients’ brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional – chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.

Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor's ability to participate in activities of daily living. Recent research suggests the use of brain-computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of motor related brain signals (EEG measured sensorimotor rhythm desynchronization), and improves movement, as measured by the Action Research Arm Test (ARAT). A sample of 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18-30 h of intervention with a novel electroencephalogram-based BCI-driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the user's input to the EEG-BCI-FES device modulates horizontal movement of a virtual cursor and also facilitates concurrent stimulation of the impaired UE. Outcome measures of function and capacity were assessed at baseline, mid-therapy, and at completion of therapy while EEG was recorded only during intervention sessions. A significant increase in r-squared values [reflecting Mu rhythm (8-12 Hz) desynchronization as the result of attempted movements of the impaired hand] presented post-therapy compared to baseline. These findings suggest that intervention corresponds with greater desynchronization of Mu rhythm in the ipsilesional hemisphere during attempted movements of the impaired hand and this change is related to changes in behavior as a result of the intervention. BCI intervention may be an effective way of addressing the recovery of a stroke impaired UE and studying neuromechanical coupling with motor outputs. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02098265.

Stroke is a leading cause of acquired disability resulting in distal upper extremity functional motor impairment. Stroke mortality rates continue to decline with advances in healthcare and medical technology. This has led to an increased demand for advanced, personalized rehabilitation. Survivors often experience some level of spontaneous recovery shortly after their stroke event, yet reach a functional plateau after which there is exiguous motor recovery. Nevertheless, studies have demonstrated the potential for recovery beyond this plateau. Non-traditional neurorehabilitation techniques, such as those incorporating the brain-computer interface (BCI), are being investigated for rehabilitation. BCIs may offer a gateway to the brain's plasticity and revolutionize how humans interact with the world. Non-invasive BCIs work by closing the proprioceptive feedback loop with real-time, multi-sensory feedback allowing for volitional modulation of brain signals to assist hand function. BCI technology potentially promotes neuroplasticity and Hebbian-based motor recovery by rewarding cortical activity associated with sensory-motor rhythms through use with a variety of self-guided and assistive modalities.

Neuroticism, a core personality trait characterized by a tendency towards experiencing negative affect, has been reported to be higher in people with temporal lobe epilepsy (TLE) compared with healthy individuals. Neuroticism is a known predictor of depression and anxiety, which also occur more frequently in people with TLE. The purpose of this study was to identify abnormalities in whole-brain resting-state functional connectivity in relation to neuroticism in people with TLE and to determine the degree of unique versus shared patterns of abnormal connectivity in relation to elevated symptoms of depression and anxiety. Ninety-three individuals with TLE (55 females) and 40 healthy controls (18 females) from the Epilepsy Connectome Project (ECP) completed measures of neuroticism, depression, and anxiety, which were all significantly higher in people with TLE compared with controls. Resting-state functional connectivity was compared between controls and groups with TLE with high and low neuroticism using analysis of variance (ANOVA) and t-test. In secondary analyses, the same analytics were performed using measures of depression and anxiety and the unique variance in resting-state connectivity associated with neuroticism independent of symptoms of depression and anxiety identified. Increased neuroticism was significantly associated with hyposynchrony between the right hippocampus and Brodmann area (BA) 9 (region of prefrontal cortex (PFC)) (p 0.005), representing a unique relationship independent of symptoms of depression and anxiety. Hyposynchrony of connection between the right hippocampus and BA47 (anterior frontal operculum) was associated with high neuroticism and with higher depression and anxiety scores (p 0.05), making it a shared abnormal connection for the three measures. In conclusion, increased neuroticism exhibits both unique and shared patterns of abnormal functional connectivity with depression and anxiety symptoms between regions of the mesial temporal and frontal lobe.

Introduction: Many stroke survivors are often left with persistent upper extremity motor impairment that affect their overall quality of life. This study aims to evaluate the efficacy of using non-invasive electroencephalographic (EEG) based brain-computer interface (BCI) therapy to facilitate distal upper extremity motor function. Data was acquired from 18 participants ((mean age = 60 years, F = 9, M = 9, 5 = sub-chronic (over 1 year from stroke onset), 13 = chronic (less than 1 year)). Behavioral outcomes were measured using the Modified Ashworth Scale (MAS), a Motor Activity Log (MAL), and the Stroke Impact Scale (SIS). We investigated whether severely-impaired, chronic stroke survivors could use BCI treatment to recover from stroke at a rate similar to that of severely-impaired, sub-chronic stroke survivors after BCI intervention. Methods: EEG data was acquired using BCI2000 software and 16 channel EEG cap with electrodes positioned according to the standard 10-20 system over the sensorimotor cortex at C3 & C4. Participants were cued to move either their left or right hand to control a cursor towards the right or left side of a monitor. Data acquired at baseline (no more than one week prior to BCI therapy) and post-therapy (no more than one week following BCI therapy) were used in this analyses. Results: No significant decrease in improvement in the chronic group compared to the sub-chronic group (0.95 confidence interval) was observed. The chronic group showed significant improvement in their affected arm in: MAS elbow flexion (p = 0.03746), MAS finger extension (p = 0.01896), MAL arm use (p = 0.02065), MAL how well they could use their arm (p = 0.02065), SIS perceived strength (p = 0.04105), and SIS arm use for daily life tasks (p = 0.01741). Conclusion: For chronic stroke survivors, BCI intervention for restoration of upper extremity motor function may promote recovery at a rate similar to that of sub-chronic survivors.

Background: Changes in cortical gray matter volume is associated with cognitive impairment after ischemic stroke. Here, we investigated the differences of whole brain gray matter volume and gray matter density between acute stroke patients (AS), patients with risk factors for stroke (RS), and healthy controls (HC). Methods: Patients with acute stroke (N = 65, ischemic stroke within 8 days of stroke onset, mean age = 60.64 years, SD = 12.24, SEM = 1.71), patients with risk factors for stroke (N = 27, mean age = 64.12 years, SD = 11.96, SEM = 2.30) and age-matched healthy controls (N = 57, mean age = 58.05 years, SD = 6.06, SEM = 0.80) were scanned on a GE 3T MRI scanner. High-resolution 3D T1-weighted whole brain BRAVO FSPGR images were collected: repetition time = 8160 ms, echo time = 3.18 ms, flip angle = 12°, field-of-view = 100 х 100 mm, slice thickness = 1 mm. Gray matter volume and density changes were assessed using VBM8 toolbox and SPM8, with Gaussian Random Field as multiple comparison (voxel p < .001, cluster p < .05). Results: Two sample t-test indicated that: (1) AS had significantly decreased gray matter volume compared to RS (only on the postcentral gyrus) and HC (in sub-regions of frontal, temporal, occipital lobes, sub-lobar, cerebellum); (2) AS also had significantly decreased gray matter density compared to RS (in sub-regions of frontal, temporal, parietal, occipital lobes, limbic lobe) and to HC (in sub-regions of frontal, temporal, occipital lobes, sub-lobar, limbic lobe, cerebellum) but except two regions with increased density compared to HC (anterior and posterior cingulum). (3) RS had both significantly decreased gray matter volume and density compared to HC (in sub-regions of frontal, temporal, occipital lobes, sub-lobar, cerebellum). Conclusion: Compared to RS and HC, AS may induce changes in both gray matter volume and density, which result in subsequent cognitive impairments such as language or working memory. Significant changes in RS indicate that brain and associated behavioral changes in this population merits further investigation before progression to stroke.

Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor's ability to participate in activities of daily living. Recent research suggests the use of brain-computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of functional connectivity, and improves movement, as measured by the Action Research Arm Test (ARAT). 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18 - 30 hours of intervention with a novel electroencephalogram based BCI driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the EEG-BCI-FES device modulates horizontal movement of a virtual cursor as well as facilitating concurrent FES stimulation of only the impaired upper extremity. Primary outcome measure of function, ARAT was assessed at baseline, mid-therapy, and at completion of therapy. The signed r-squared value (at the ipsilesional C4 or C3 sites) for the Mu (8-12Hz) rhythm significantly decreased in the post-therapy stage compared to the pre-therapy stage (one-tailed paired t-test: t(20)= 1.85; p = 0.039; meanPRE = -0.142; meanPOST = -0.161), while the subject attempted movements of the impaired hand. These findings suggests that as the result of the intervention sessions, the “desynchronization” of the Mu rhythm signals significantly increases post-therapy at the ipsilesional motor site and this change is related to changes in behavior as a result of intervention. BCI intervention may be an effective way of addressing the stroke recovery of a stroke impaired upper extremity.

Despite the established effectiveness of the brain-computer interface (BCI) therapy during stroke rehabilitation (Song et al., 2014a, 2015; Young et al., 2014a,b,c, 2015; Remsik et al., 2016), little is understood about the connections between motor network reorganization and functional motor improvements. The aim of this study was to investigate changes in the network reorganization of the motor cortex during BCI therapy. Graph theoretical approaches are used on resting-state functional magnetic resonance imaging (fMRI) data acquired from stroke patients to evaluate these changes. Correlations between changes in graph measurements and behavioral measurements were also examined. Right hemisphere chronic stroke patients (average time from stroke onset = 38.23 months, standard deviation (SD) = 46.27 months, n = 13, 6 males, 10 right-handed) with upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device. Eyes-closed resting-state fMRI (rs-fMRI) scans, along with T-1 weighted anatomical scans on 3.0T MRI scanners were collected from these patients at four test points. Immediate therapeutic effects were investigated by comparing pre and post-therapy results. Results displayed that th average clustering coefficient of the motor network increased significantly from pre to post-therapy. Furthermore, increased regional centrality of ipsilesional primary motor area (p = 0.02) and decreases in regional centrality of contralesional thalamus (p = 0.05), basal ganglia (p = 0.05 in betweenness centrality analysis and p = 0.03 for degree centrality), and dentate nucleus (p = 0.03) were observed (uncorrected). These findings suggest an overall trend toward significance in terms of involvement of these regions. Increased centrality of primary motor area may indicate increased efficiency within its interactive network as an effect of BCI therapy. Notably, changes in centrality of the bilateral cerebellum regions have strong correlations with both clinical variables [the Action Research Arm Test (ARAT), and the Nine-Hole Peg Test (9-HPT)].

Background: Advanced neuroimaging measures along with clinical variables acquired during standard imaging protocols provide a rich source of information for brain tumor patient treatment and management. Machine learning analysis has had much recent success in neuroimaging applications for normal and patient populations and has potential, specifically for brain tumor patient outcome prediction. The purpose of this work was to construct, using the current patient population distribution, a high accuracy predictor for brain tumor patient outcomes of mortality and morbidity (i.e., transient and persistent language and motor deficits). The clinical value offered is a statistical tool to help guide treatment and planning as well as an investigation of the influential factors of the disease process. Methods: Resting state fMRI, diffusion tensor imaging, and task fMRI data in combination with clinical and demographic variables were used to represent the tumor patient population (n = 62; mean age = 51.2 yrs.) in a machine learning analysis in order to predict outcomes. Results: A support vector machine classifier with a t-test filter and recursive feature elimination predicted patient mortality (18-month interval) with 80.7% accuracy, language deficits (transient) with 74.2%, motor deficits with 71.0%, language outcomes (persistent) with 80.7% and motor outcomes with 83.9%. The most influential features of the predictors were resting fMRI connectivity, and fractional anisotropy and mean diffusivity measures in the internal capsule, brain stem and superior and inferior longitudinal fasciculi. Conclusions: This study showed that advanced neuroimaging data with machine learning methods can potentially predict patient outcomes and reveal influential factors driving the predictions. Keywords: Machine-learning, fMRI, DTI, Tumor patients, Outcome prediction

The primary goal of this work was to apply data-driven machine learning regression to assess if resting state functional connectivity (rs-FC) could estimate measures of behavioral domains in stroke subjects who completed brain-computer interface (BCI) intervention for motor rehabilitation. The study cohort consisted of 20 chronic-stage stroke subjects exhibiting persistent upper-extremity motor deficits who received the intervention using a closed-loop neurofeedback BCI device. Over the course of this intervention, resting state functional MRI scans were collected at four distinct time points: namely, pre-intervention, mid-intervention, post-intervention and 1-month after completion of intervention. Behavioral assessments were administered outside the scanner at each time-point to collect objective measures such as the Action Research Arm Test, Nine-Hole Peg Test, and Barthel Index as well as subjective measures including the Stroke Impact Scale. The present analysis focused on neuroplasticity and behavioral outcomes measured across pre-intervention, post-intervention and 1-month post-intervention to study immediate and carry-over effects. Rs-FC, changes in rs-FC within the motor network and the behavioral measures at preceding stages were used as input features and behavioral measures and associated changes at succeeding stages were used as outcomes for machine-learning-based support vector regression (SVR) models. Potential clinical confounding factors such as age, gender, lesion hemisphere, and stroke severity were included as additional features in each of the regression models. Sequential forward feature selection procedure narrowed the search for important correlates. Behavioral outcomes at preceding time-points outperformed rs-FC-based correlates. Rs-FC and changes associated with bilateral primary motor areas were found to be important correlates of across several behavioral outcomes and were stable upon inclusion of clinical variables as well. NIH Stroke Scale and motor impairment severity were the most influential clinical variables. Comparatively, linear SVR models aided in evaluation of contribution of individual correlates and seed regions while non-linear SVR models achieved higher performance in prediction of behavioral outcomes.

Introduction: Post-stroke neuropsychological evaluation can take a long time to assess impairments in subjects with no overt clinical deficits. We focus on language network based on a five-minute non-invasive resting state functional MRI (rs-fMRI) to identify subclinical language deficit (SLD) using a machine learning classifier. Such a system could supplement neuropsychological test to detect SLD from functional connectivity (FC). Predictive ability of FC derived from slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands are compared to that derived from all low frequency oscillations (LFO; 0.01-0.1 Hz). Methods: Sixty clinically non-aphasic, right-handed subjects were categorized into three age and gender matched groups based on stroke status, and normed verbal fluency score (VFS): 20 ischemic stroke subjects at higher risk of SLD (LD+; mean VFS = -1.77, mean age = 63.1 years) and 20 ischemic stroke subjects at lower risk of SLD (LD-; mean VFS = -0.05, mean age = 58.35 years), 20 healthy controls (HC; mean VFS = 0.21, mean age = 57.4 years). Anatomical T1-weighted and rs-fMRI scans were acquired within 14 days of stroke onset. A mask of 23 regions of interest (ROIs) was selected from the language network and blood-oxygen-level-dependent (BOLD) signal was extracted from each ROI corresponding to LFO, slow-4 and slow-5 bands. FC was computed by correlating time series from pairs of ROIs for all three frequency bands. A binary support vector machine (SVM) used 253 correlation indices to classify between groups: (a) LD+ vs LD-, (b) LD- vs HC (c) HC vs LD+. The classifier performance was assessed using leave-one-out cross-validation (LOOCV) with a univariate filter based feature selection. Results: LOOCV accuracies upon appropriate feature selection are reported by frequency band: Slow-4 band: 82.5% for (a), 90% for (b), 90% for (c). Slow-5 band: 85% for (a), 90 % for (b), 92.5% for (c). LFO: 82.5% for (a), 87.5% for (b), 90% for (c). Conclusion: Subjects at risk of SLD can be differentiated from healthy controls based on rs-fMRI with a high accuracy classifier, which otherwise could take longer to identify through neuropsychological assessment. Additionally, BOLD signal in the slow-5 band better characterized FC by providing a higher classification accuracy.

While most survivors have some spontaneous recovery after stroke, they reach a functional plateau and are left with persistent motor impairments. An emerging therapy, EEG-based Brain-Computer Interface (BCI), shows promise in promoting neural reorganization and facilitating additional motor recovery after stroke, however, the relationships between the neuroplastic changes and behavioral outcomes following this therapy are not fully understood. We examined changes in resting-state functional connectivity (rsFC) in the motor network and behavioral measures, including Stroke Impact Scale (SIS) and Action Research Arm Test (ARAT), over the course this therapy and investigated functional connectivity - behavior correlations. Twenty-six stroke patients with mild to severe upper extremity impairment completed EEG-based BCI therapy. Resting fMRI and anatomical scans were acquired on a GE 3T MRI Scanner before therapy, mid-, post- and one-month post-therapy, along with a neuropsychology battery. MRI scans of right hemisphere stroke patients were flipped to treat all subjects as left lesion hemisphere patients. We performed whole network, inter-hemispheric and intra-hemispheric seed region based connectivity analyses to study changes in rsFC over time and identify correlations between changes in rsFC and behavior over time. After therapy, a significant increase in network connectivity (p =0.000003) and intra-hemispheric connectivity (p = 0.047) from pre- to one-month post-therapy was observed. Additionally, inter-hemispheric connectivity increased and trended towards significance (p = 0.058). A significant positive correlation was observed between changes in network-level rsFC and SIS ADL (p = 0.005). Changes in intra-hemispheric rsFC correlated with ARAT for the affected arm (p = 0.001) and SIS Mobility (p = 0.003). The results suggest that EEG-based BCI therapy facilitates changes in rsFC in the motor network in stroke survivors, and these changes in connectivity are correlated with improvements in behavioral outcomes. This analysis provides a foundation for furthering our understanding of the potential of EEG-based BCI as a therapeutic modality for stroke rehabilitation to promote neurophysiological changes and motor recovery.

A helicopter's vertical takeoff and landing as well as its ability to hover, make it ideal for carrying a suspended load, which facilitates several applications in military and in places that are inaccessible by humans. This paper considers a helicopter having a suspended load that leads to the degradation of the helicopter control due to the increase in work load, which in worst case may lead to instability or a serious accident. This work proposes an H-infinity controller for a helicopter-slung load system in hovering condition. The controller used in this paper robustly stabilizes the helicopter even in the presence of load oscillations. This work shows sufficient control action that must be given by the helicopter in order to reduce the load swing and the results also show in the response on the effect of load swing with and without the controller.

Highlights • Cognitive impairment is a major comorbidity of temporal lobe epilepsy (TLE). • Three discrete cognitive phenotypes of TLE are identified here. • The phenotypes are linked to network, clinical, and socioeconomic characteristics. • This taxonomy advances clinical and theoretical understanding of the cognitive complications of TLE., This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P

Little is known about the underlying neural structures that mediate the generation and tracking of discourse referents. In two functional magnetic resonance imaging experiments, we examined the neural structures involved in generating and maintaining the representations of multiple referents. Experiment 1 used two-sentence discourses with singular and plural conditions linking back to single or conjoined subjects. In Experiment 2, conjunction type was manipulated in order to keep the number of discourse entities constant across the discourse. Both experiments found greater activation in the superior parietal lobule bilaterally for plural entities relative to singular entities in Experiment 1 and for unconjoined plural entities relative to conjoined plural entities in Experiment 2. This parietal activation suggests that referring to multiple entities evokes multiple representations that need to be integrated and tracked. We discuss these findings in terms of psycholinguistic theories of multiple referent representations.

Nine year old girl was presented with paroxysmal episodes of hypertensive emergency. She was asymptomatic with normal blood pressure without antihypertensives in between the episodes. MRI brain was suggestive of reversible posterior leukoencephalopathy. Acute episodes were managed with IV labetalol infusion and amlodipine. She was evaluated extensively to find out the etiology of hypertension. Cardiac and renal causes were ruled out. Work up for pheochromocytoma, hyperaldosteronism, porphyria and vasculitis were negative. The case is reported in view of the rare presentation and the leukoencephalopathy noted in this case.

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. In our present study, we examine the test-retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5 min up to 13 min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.

Introduction: Despite its prevalence in stroke research, the vascular dependence of functional MRI (fMRI) makes it susceptible to changes in factors such as vascular reactivity that may occur over the course of stroke recovery, potentially confounding longitudinal findings. Scaling fMRI data with respect to vascular properties may account for some of the high variability often observed in patterns of motor task activation throughout recovery. Hypothesis: We hypothesized that scaling fMRI data by the amplitude of resting-state fluctuations (RSFA), an estimate of vascular reactivity, would reduce intersubject variability of motor task activation in both patients and similarly-aged controls while minimizing between-group differences and revealing a more consistent pattern of changes in activation associated with stroke recovery. Methods: 24 ischemic stroke patients (ages 44-84 years, mean = 64) were scanned at early (2-12 days, mean = 5) and late (mean = 11 months) stages after onset. 20 healthy controls (ages 47-74 years, mean = 59) were also scanned, 11 of whom were scanned again after ~6 months. Subjects performed two 20-second-block finger-tapping tasks, one with each hand, and a 10-minute resting-state fMRI scan, from which the scaling factor (RSFA) was computed. Percent signal change and activation volume before and after scaling were measured across several bilateral motor areas and compared between groups and within each group longitudinally. Results: Scaling with RSFA significantly reduced intersubject variability of intensity and extent of activation in both groups while mitigating between-group differences in activation in multiple motor areas. Further, scaling revealed a significant reduction of activation across motor regions in stroke patients over time that was not apparent without scaling. This pattern more closely resembled that of controls, for which it was evident both before and after scaling. Conclusions: Our findings point to a role for fMRI calibration in longitudinal studies of plasticity and recovery after stroke, and suggest that the large variability often observed in activation patterns associated with recovery may in part be vascular in origin.

Post-stroke neuropsychological evaluation can take a long time to assess motor, cognitive, language impairments, etc. in subjects with no overt clinical deficits. A ten-minute non-invasive diffusion tensor imaging (DTI) can potentially pick subtle impairments like subclinical language deficit. Machine learning can supplement neuropsychological test to detect language deficit from white matter tracts in post-stroke DTI with high accuracy. Thirty subjects were categorized into three groups based on stroke status and language neuropsychological test (verbal fluency task): ten healthy controls, ten ischemic stroke subjects with language deficit (normed verbal fluency score < -1.5) and ten ischemic stroke subjects with no language deficit (normed fluency score >= -1.5). Subjects were age and gender matched across groups. DTI with 56 directions acquired on 3T GE MRI 750 scanners were processed using standard steps to generate fractional anisotropy (FA) maps that measure microstructural integrity of the brain. Individual 48 white matter tracts were extracted using Johns Hopkins University (JHU) ICBM-DTI-81 white matter atlas. Mean FA per tract per subject was computed. A support vector machine (SVM) used mean FA to classify among groups. With a Gaussian kernel and leave-one-out cross validation, SVM ranked tracts predicting language deficit. Groups were age and gender matched resulting in no significant difference in the age across groups ( p > 0.05). While a three-class SVM yields a 57% classification accuracy, binary SVM on pairs of groups provides accuracy of 80% (healthy controls vs. stroke with language deficit), 85% (healthy controls vs. stroke with no language deficit) and 75% (stroke with language deficit vs. stroke with no language deficit). Top independent white matter tract with highest weight given by classifier was: splenium of corpus callosum, right superior corona radiata, right external capsule respectively for the three classifications above. In conclusion, SVM classifier can predict whether a given subject has language deficit from post-stroke DTI with accuracy significantly higher than random chance. Weights obtained from SVM indicate importance of specific tracts driving the classifier and distinguishing the groups.

Introduction: This current study is part of a larger, on-going clinical trial (NCT02098265), which evaluates non-invasive electroencephalographic (EEG) based brain-computer interface(BCI) therapy for restoration of distal upper extremity motor function in stroke survivors. We acquired data for 8 participants (mean age= 64 years) presenting with varying levels of upper-extremity motor deficits and chronicity since stroke. EEG based BCI task-related performance outcomes were compared with distal extremity behavioral testing on the 9-Hole Peg Test - 9HPT) to illustrate the relationship of BCI training performance on behavioral performance. Methods: EEG data is acquired with BCI2000, a 16 channel recording system (g.LADYbird-g. GAMMAsys-g. USBamp, Guger Technologies, Graz, Austria) with electrodes positioned according to the standard 10-20 system over the sensorimotor cortex at C3 & C4. Participants performed a hand movement task; randomly cued to move either their affected or unaffected hand depending on the appearance of virtual target and cursor on the screen. 9HPT Data was selected from two BCI conditions: 1) BCI with visual stimulus only, and 2) BCI with visual stimulation plus functional electrical stimulation (FES) of the impaired arm, and tongue stimulation (TS) at four time-points: baseline (prior to BCI therapy), mid-point of BCI therapy, post-therapy, and one month post-therapy. Results: BCI visual plus stimulus for the unaffected side was found to best relate to 9HPT scores (p= 0.0928) with a trend to significance. BCI visual plus stimulus for the affected side was not shown to correlate with 9HPT scores (p= 0.2655). The results suggest that 9HPT scores better relate with average task accuracy when the TS and FES adjuvants are incorporated (BCI stimulus vs 9H PT unaffected p= 0.09284, BCI stimulus vs 9H PT affected p= 0.26553) compared to the BCI visual only task (BCI visual vs 9HPT unaffected p=0.2702, BCI visual vs 9HPT affected (p= 0.89276). Conclusion: Non-invasive EEG-based BCI therapy may be suitable for the restoration of distal upper extremity motor function but average task accuracy does not appear to be a valid indicator of motor improvement. This finding suggests that visual only BCI systems are inadequate for motor rehabilitation.

The goal of this work is to evaluate if changes in brain connectivity can predict behavioral changes among subjects who have suffered stroke and have completed brain-computer interface (BCI) interventional therapy. A total of 23 stroke subjects, with persistent upper-extremity motor deficits, received the stroke rehabilitation therapy using a closed-loop neurofeedback BCI device. Over the course of the entire interventional therapy, resting-state fMRI were collected at two time points: prior to start and immediately upon completion of therapy. Behavioral assessments were administered at each time point via neuropsychological testing to collect measures on Action Research Arm Test, Nine-Hole Peg Test, Barthel Index and Stroke Impact Scale. Resting-state functional connectivity changes in the motor network were computed from pre- to post-interventional therapy and were combined with clinical data corresponding to each subject to estimate the change in behavioral performance between the two time-points using a machine learning based predictive model. Inter-hemispheric correlations emerged as stronger predictors of changes across multiple behavioral measures in comparison to intra-hemispheric links. Additionally, age predicted behavioral changes better than other clinical variables such as gender, pre-stroke handedness, etc. Machine learning model serves as a valuable tool in predicting BCI therapy-induced behavioral changes on the basis of functional connectivity and clinical data.

Functional magnetic resonance imaging studies have significantly expanded the field’s understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RNS) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor and executive control resting state networks from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects). ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron). The method’s utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians’ expert interpretation.

The Switched Reluctance Motor is a simple and robust motor which is having many applications in the fields where varying power and speed ranges are essential. The Switched reluctance motor having no rotor windings and it is free to rotate between the magnetic field produced by the stator poles. The torque is developed by the tendency of the rotor to align with the stator magnetic field. The torque is controlled by the controlled excitation of stator filed windings either by mechanical or electrical means. The turn on time for each stator phase is a crucial part in the motor speed control. Due to the inherent non uniform torque output characteristics a higher torque ripple is present in the motor output unless a torque ripple minimization strategy is used. Various converter circuits are introduced for reducing the torque ripples by the controlled firing of valves. In this paper two of such commonly based methods are studied and compared to find out the best practices among them which favors the maximum torque ripple reduction.