Relationship between functional connectivity and weight-gain risk of antipsychotics in schizophrenia.

BACKGROUND: The mechanisms by which antipsychotic medications (APs) contribute to obesity in schizophrenia are not well understood. Because AP effects on functional brain connectivity may contribute to weight effects, the current study investigated how AP-associated weight-gain risk relates to functional connectivity in schizophrenia. METHODS: Fifty-five individuals with schizophrenia (final N = 54) were divided into groups based on previously reported AP weight-gain risk (no APs/low risk [N = 19]; moderate risk [N = 17]; high risk [N = 18]). Resting-state functional magnetic resonance imaging (fMRI) was completed after an overnight fast ("fasted") and post-meal ("fed"). Correlations between AP weight-gain risk and functional connectivity were assessed at the whole-brain level and in reward- and eating-related brain regions (anterior insula, caudate, nucleus accumbens). RESULTS: When fasted, greater AP weight-gain risk was associated with increased connectivity between thalamus and sensorimotor cortex (pFDR = 0.021). When fed, greater AP weight-gain risk was associated with increased connectivity between left caudate and left precentral/postcentral gyri (pFDR = 0.048) and between right caudate and multiple regions, including the left precentral/postcentral gyri (pFDR = 0.001), intracalcarine/precuneal/cuneal cortices (pFDR < 0.001), and fusiform gyrus (pFDR = 0.008). When fed, greater AP weight-gain risk was also associated with decreased connectivity between right anterior insula and ventromedial prefrontal cortex (pFDR = 0.002). CONCLUSIONS: APs with higher weight-gain risk were associated with greater connectivity between reward-related regions and sensorimotor regions when fasted, perhaps relating to motor anticipation for consumption. Higher weight-gain risk APs were also associated with increased connectivity between reward, salience, and visual regions when fed, potentially reflecting greater desire for consumption following satiety.

Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review.

OBJECTIVE: To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field. BACKGROUND: Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis. METHODS/RESULTS: The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field. CONCLUSION: Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.

Mapping Sex-Specific Neurodevelopmental Alterations in Neurite Density and Morphology in a Rat Genetic Model of Psychiatric Illness.

Neurite orientation dispersion and density imaging (NODDI) is an emerging magnetic resonance (MR) diffusion-weighted imaging (DWI) technique that permits non-invasive quantitative assessment of neurite density and morphology. NODDI has improved our ability to image neuronal microstructure over conventional techniques such as diffusion tensor imaging (DTI) and is particularly suited for studies of the developing brain as it can measure and characterize the dynamic changes occurring in dendrite cytoarchitecture that are critical to early brain development. Neurodevelopmental alterations to the diffusion tensor have been reported in psychiatric illness, but it remains unknown whether advanced DWI techniques such as NODDI are able to sensitively and specifically detect neurodevelopmental changes in brain microstructure beyond those provided by DTI. We show, in an extension of our previous work with a Disc1 svΔ2 rat genetic model of psychiatric illness, the enhanced sensitivity and specificity of NODDI to identify neurodevelopmental and sex-specific changes in brain microstructure that are otherwise difficult to observe with DTI and further corroborate observed changes in brain microstructure to differences in sex-specific systems-level animal behavior. Together, these findings inform the potential application and clinical translational utility of NODDI in studies of brain microstructure in psychiatric illness throughout neurodevelopment and further, the ability of advanced DWI methods such as NODDI to examine the role of biological sex and its influence on brain microstructure in psychiatric illness.

Microglial Density Alters Measures of Axonal Integrity and Structural Connectivity.

Diffusion tensor imaging (DTI) has fundamentally transformed how we interrogate diseases and disorders of the brain in neuropsychiatric illness. DTI and recently developed multicompartment diffusion-weighted imaging (MC-DWI) techniques, such as NODDI (neurite orientation dispersion and density imaging), measure diffusion anisotropy presuming a static neuroglial environment; however, microglial morphology and density are highly dynamic in psychiatric illness, and how alterations in microglial density might influence intracellular measures of diffusion anisotropy in DTI and MC-DWI brain microstructure is unknown. To address this question, DTI and MC-DWI studies of murine brains depleted of microglia were performed, revealing significant alterations in axonal integrity and fiber tractography in DTI and in commonly used MC-DWI models. With accumulating evidence of the role of microglia in neuropsychiatric illness, our findings uncover the unexpected contribution of microglia to measures of axonal integrity and structural connectivity and provide unanticipated insights into the potential influence of microglia in diffusion imaging studies of neuropsychiatric disease.

Alterations in brain white matter microstructural properties in patients with Crohn's disease in remission.

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.

A Verbal Fluency Task-Based Brain Activation fMRI Study in Patients with Crohn's Disease in Remission.

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 19-63 years) and 20 HCs (matched in age and gender) were analyzed. Task functional MRI (fMRI) data were 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 subset 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 CD 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 the influence of factors, such as chronicity of the disease and medication effects on task-associated brain activation patterns in this patient population.

Alterations in resting-state functional connectivity in patients with Crohn's disease in remission.

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.

Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation.

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.

Evaluation of Changes in the Motor Network Following BCI Therapy Based on Graph Theory Analysis.

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)].

Behavioral Outcomes Following Brain-Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial.

Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain-computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as "responders" (<1 = "non-responders") and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotal score at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p = 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors. Clinical Trial Registration: ClinicalTrials.gov, NCT02098265.

Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning.

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.

Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.

Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.

Role of the Contralesional vs. Ipsilesional Hemisphere in Stroke Recovery.

Following a stroke, the resulting lesion creates contralateral motor impairment and an interhemispheric imbalance involving hyperexcitability of the contralesional hemisphere. Neuronal reorganization may occur on both the ipsilesional and contralesional hemispheres during recovery to regain motor functionality and therefore bilateral activation for the hemiparetic side is often observed. Although ipsilesional hemispheric reorganization is traditionally thought to be most important for successful recovery, definitive conclusions into the role and importance of the contralesional motor cortex remain under debate. Through examining recent research in functional neuroimaging investigating motor cortex changes post-stroke, as well as brain-computer interface (BCI) and transcranial magnetic stimulation (TMS) therapies, this review attempts to clarify the contributions of each hemisphere toward recovery. Several functional magnetic resonance imaging studies suggest that continuation of contralesional hemisphere hyperexcitability correlates with lesser recovery, however a subset of well-recovered patients demonstrate contralesional motor activity and show decreased functional capability when the contralesional hemisphere is inhibited. BCI therapy may beneficially activate either the contralesional or ipsilesional hemisphere, depending on the study design, for chronic stroke patients who are otherwise at a functional plateau. Repetitive TMS used to excite the ipsilesional motor cortex or inhibit the contralesional hemisphere has shown promise in enhancing stroke patients' recovery.

Parameters From the Complete Phonatory Range of an Excised Rabbit Larynx.

OBJECTIVE: This study aims to collect data throughout the complete phonatory range using rabbit larynges. STUDY DESIGN: This is a methodological excised rabbit larynx study. METHODS: Seven rabbit larynges were dissected and mounted on a modified excised laryngeal apparatus. Phonation was initiated at phonation threshold pressure (PTP) and airflow was increased by consistent increments until phonation instability pressure (PIP) was reached. At each airflow level, aerodynamic measurements, acoustic recordings, and high-speed videos were recorded. This procedure was repeated at multiple elongation conditions to further explore the parameters. Data were then compared across subjects and elongation conditions. RESULTS: At PTP, subglottal pressure, fundamental frequency, and sound pressure level were found to increase significantly as elongation was increased. As elongation was increased at PIP, airflow was found to significantly decrease, whereas fundamental frequency was found to significantly increase. Vibratory amplitude decreased at both PTP and PIP as elongation increased. Also, as elongation increased, the range of all parameters was found to decrease significantly. CONCLUSIONS: The results obtained, combined with the similarities of the histologic structure of the vocal fold lamina propria between rabbits and humans, validate the rabbit larynx as an effective and reliable model for tissue inflammation studies.

The Development of Augmented Reality to Enhance Minimally Invasive Surgery.

Minimally invasive surgery (MIS) reduces unnecessary tissue damage to the patient but obscures the natural surgical interface that is provided by open surgical procedures. Multiple feedback mechanisms, mainly visual and tactile, are greatly reduced in MIS. Microscopes, endoscopes, and image-guided navigation traditionally provide enough visual information for successful minimally invasive procedures, although the limited feedback makes these procedures more difficult to learn. Research has been performed to develop alternative solutions that regain additional feedback. Augmented reality (AR), a more recent guidance innovation that overlays digital visual data physically, has begun to be implemented in various applications to improve the safety and efficacy of minimally invasive procedures. This review focuses on the recent implementation of augmented display and direct visual overlay and discusses how these innovations address common feedback concerns associated with minimally invasive surgeries.

An Objective Parameter for Quantifying the Turbulent Noise Portion of Voice Signals.

OBJECTIVES: Currently, there are no objective measures capable of distinguishing between all four voice signal types proposed by Titze in 1995 and updated by Sprecher in 2010. We propose an objective metric that distinguishes between voice signal types based on the aperiodicity present in a signal. STUDY DESIGN: One hundred fifty voice signal samples were randomly selected from the Disordered Voice Database and subjectively sorted into the appropriate voice signal category on the basis of the classification scheme presented in Sprecher 2010. METHODS: Short-time Fourier transform was applied to each voice sample to produce a spectrum for each signal. The spectrum of each signal was divided into 250 time segments. Next, these segments were compared to each other and used to calculate an outcome named spectrum convergence ratio (SCR). Finally, the mean SCR was calculated for each of the four voice signal types. RESULTS: SCR was capable of significantly differentiating between each of the four voice signal types (P < 0.001). Additionally, this new parameter proved equally as effective at distinguishing between voice signal types as currently available parameters. CONCLUSION: SCR was capable of objectively distinguishing between all four voice signal types. This metric could be used by clinicians to quickly and efficiently diagnose voice disorders and monitor improvements in voice acoustical signals during treatment methods.