Spatial Neglect and Delirium After Stroke
Spatial Neglect and Delirium After Stroke
The goal of this study is to understand what brain mechanisms become disrupted when stroke
 survivors experience delirium. Delirium is an acute reduction in attention and cognition,
 associated with poor recovery, longer hospitalization and even death. One major factor
 increasing the risk of delirium after stroke may be spatial neglect occurring after stroke on
 the right side of the brain. Spatial neglect affects awareness, orientation, and movement.
 The study will test the hypothesis that the right-dominant brain networks for arousal and
 attention are affected in both of these disorders. It is expected that the activity and
 structural integrity of these brain networks will correlate with behavioral signs and
 severity of delirium and spatial neglect. To test this hypothesis, the study will measure
 spatial neglect and delirium symptoms in 45 acute (NYC Health + Hospitals/Bellevue ) and 30
 subacute (Kessler Institute for Rehabilitation) stroke survivors and evaluate brain scans for
 these participants. This study may contribute to knowledge about brain bio-markers of
 delirium, which will greatly aid in delirium detection in stroke and other disorders.
Delirium assessment and prevention has a tremendous impact on hospitalization outcomes and
 health care costs. Delirium is a multi-component syndrome characterized by an acute reduction
 in cognitive functioning, affecting awareness, thinking, attention, and memory. Stroke
 survivors, representing 17% of the US population aged 65 and over (CDC, 2012), are at major
 risk for developing delirium (up to 50% incidence in right-hemisphere stroke). Further, about
 50% of right-hemisphere stroke patients experience spatial neglect, impairing safety and
 recovery. This study investigates a potential neural mechanism explaining the high incidence
 of both delirium and spatial neglect after right-hemisphere stroke. The study hypothesis is
 that the brain networks for arousal and attention, comprised of ascending projections from
 the mesencephalic reticular formation and integrating right-dominant dorsal and ventral
 cortical and limbic components, may be affected in these disorders. The study assesses
 magnetic resonance imaging (MRI) and behavioral data in right hemisphere stroke survivors. It
 is predicted that impaired activity and structural integrity of the brain networks for
 arousal and attention will correlate with behavioral signs of delirium and spatial neglect.
 The study will be conducted at two sites recruiting an acute (n=45) and a subacute (n=30)
 patient samples. The findings of this study have the potential to impact stroke care by
 providing a critical biomarker and behavioral profile of post-stroke delirium. This may alert
 clinicians to initiating preventive care and targeted interventions in patients who are at
 high risk of hospital morbidity and loss of independence.

 Study Hypotheses Hypothesis 1: It is hypothesized that a lesion-deficit analysis will reveal
 that in patients with delirium and/or spatial neglect, right-brain areas within the
 attention, orientation, and arousal networks are affected by stroke lesions.

 Hypothesis 2: Because it is hypothesized that both disorders stem from a dysfunction of
 common brain networks, it is expected that spatial neglect severity will be a predictor of
 delirium severity, controlling for relative lesion size and stroke severity Hypothesis 3:
 Both spatial neglect and delirium severity will be correlated with functional connectivity
 among the brain areas within the attention, orientation, and arousal networks.

 Hypothesis 4: Based on evidence from prior work in post-surgical delirium patients, neglect
 and coma patients, it is hypothesized that the integrity of white matter connections among
 brain regions comprising the cortical and subcortical networks for attention, orientation,
 and arousal is inversely correlated with the severity of delirium and spatial neglect.

 Planned Analyses:

 Hypothesis 1:

 To examine the critical network components affected in post-stroke delirium, the study will
 examine participants' structural scans. The structural scans recorded in the study will
 include a T1-weighted and a T2-weighted FLAIR image for 30 subacute stroke par-ticipants. In
 addition, clinical scans will be obtained for the 45 acute stroke participants, resulting in
 75 total brain scans. Structural lesions will be mapped using a semi-automated lesion mapping
 in MRIcron package, where each brain voxel will be scored in a binary fashion as lesioned or
 non-lesioned. Three-dimensional lesion masks will be fed into VLSM 2 software implemented in
 Matlab (Voxel Lesion Symptom Mapping). For the pseudo-continuous outcomes, such as the
 Behavioral Inattention Test (BIT) and Confusion Assessment Method-Severity (CAM-S) scores, a
 t-score will be computed testing whether lesion in a given brain voxel predicts higher or
 lower severity score. For the binary outcomes, such as delirium diagnosis, for each voxel a
 Leibermeister measure will be computed testing whether a given lesioned voxel predicts
 delirium vs. no delirium across all participants. The analysis will serially travel through
 the brain voxels and conduct a test in each voxel. The voxels will be thresholded such that
 only those that co-occur in at least 5 patients will be considered. In a typical lesion study
 this will result in hundreds of voxels considered across participants. The final results are
 corrected for multiple comparisons using False Discovery Rate of p<.05.

 Hypothesis 2:

 This hypothesis seeks to establish if any association exists between the presence and
 severity of post-stroke delirium and spatial neglect. To test this hypothesis a regression
 analysis will be conducted using the BIT score as the predictor and the CAM score as an
 outcome. The analysis will control for NIH stroke scale score and lesion size, as well as
 age. In both sets of analyses raw assessment scores will be converted to percent for scale
 uniformity.

 Hypothesis 3:

 A seed-based functional connectivity analysis will be carried out, testing for a correlation
 among time-courses of selected brain regions as a function of CAM-S and BIT scores. 16
 regions of interest (ROI) will be used to conduct this analysis within the arousal and
 attention networks. A whole brain ROI-to-voxel analysis will also be carried out to capture
 any unexpected associations with delirium and neglect severity. This analysis and data
 preprocessing will be done using CONN toolbox, a Matlab-based cross-platform software for the
 computation, display, and analysis of functional connectivity in fMRI. First, realignment
 will be performed between all successive brain volumes and the 1st volume of the series.
 Slice-timing correction will be applied to account for the time difference in the interleaved
 acquisition of brain slices. Structural segmentation will be performed to create masks of
 white matter and cerebrospinal fluid (CSF). These masks will be used in estimating a nuisance
 regressor representing physiological noise. The functional scans will be normalized (aligned)
 to the structural scans and the atlas template. This is done for group comparisons and to
 allow ROI definition using an anatomical atlas. Functional images will be smoothed with a
 6mm-radius kernel. Next, the contribution of motion outliers (large movements), continuous
 motion (roll, pitch, and yaw), and physiological noise will be regressed from the data.
 Finally, ROI-to-ROI and ROI-to-voxel connectivity will performed within each brain scan to
 allow for 2nd-order comparisons (i.e., contribution of delirium and neglect severity scores
 to functional connectivity, controlling for covariates).

 Hypothesis 4:

 A probabilistic tractography analysis will be conduced using 16 ROI, defined a priori from
 the literature. The analysis will be carried out on DTI data using diffusion tensor
 tractography tools available in the FSL analysis suite (e.g.., Probtrax, FDT Toolbox). The
 analysis is expected to identify tracts that connect regions within the attention and arousal
 networks. It will estimate voxel-wise Fractional Anisotropy (FA) as a measure of fiber
 integrity. FA is a scalar value between 0 and 1, measuring the principal diffusion direction
 of water molecules. An FA of 0 indicates a perfect sphere, i.e., uniform diffusion. Places
 where adjacent voxels have the same directional coherence of diffusion are probabilistically
 assigned to tract locations. The values are then averaged to obtain a global FA for each
 tract. Delirium and neglect severity scores will be regressed on FA values in an ANCOVA
 (accounting for clinical/demographic/physiological/patient characteristics). If the ROI-based
 approach is not successful, whole brain FA maps will be considered including areas outside
 the brain networks of interest, for an association with delirium and neglect severity scores.

 Sample Size Determination.

 A pilot investigation was conducted to estimate effect size. 19 right-brain stroke patients
 (12 females, 8 Caucasian, 8 African American, 3 Asian), aged 60 years (SD=17 years),
 comprised of 1 chronic (> 6 months post-stroke) and 18 subacute (< 1 month post-stroke)
 participated in the pilot study. R-squared value of .26 was obtained for the association
 between delirium and neglect severity. This gives an effect size (Cohen's f2) of .35. With
 this effect size, there is over 99% power to observe the effect, assuming a sample size of 75
 patients and an alpha of .05 (Hypothesis 2)

 To estimate power of our lesion deficit analysis (Hypothesis 1), data was simulated using
 15%, 30%, and 50% as proportion of the participant sample in whom the same voxel will be
 lesioned. Leibermeister approach was applied to compute p-values for testing the null
 hypothesis of no association between voxel lesion status and delirium status. With 75
 participants (Acute+Subacute Sample) there is over 80% power to detect an effect, assuming
 that at least 30 percent of the sample have the same lesion, and the lesion is between 40-100
 voxels, forming a tight cluster. These are reasonable assumptions given past studies.

 In the pilot study, a general linear model that included as regressor delirium or neglect
 severity score was applied to the 16X16 ROI connectivity measure matrix for each participant.
 The resulting t-scores for the between-subjects effects were in the range of 4.61 to 7.88 for
 the BIT measure and 7.07 to 9.31 for the CAM-S. These t-scores correspond to a large effect
 size. Using a Cohen's f of .35 (large effect) to estimate power for linear multiple
 regression, with 4 predictors, an alpha level of .05, and a two-tailed test, it is estimated
 that there will be 87% power to observe this effect, given a sample of 30 participants (Acute
 sample) (Hypothesis 3).

 A previous study reported thalamus fractional anisotropy (FA) of .315 (.026) in delirious
 compared to .333 (.023) in non-delirious patients. These values correspond to a large Cohen's
 d effect size. Assuming that FA values in a similar range will be observed, there is
 excellent power to detect these effects with a linear multiple regression model.

 Based on average patient flow, is is estimated that up to 20 patients will be screened
 monthly for study eligibility at each site. Based on recruitment rates of the pilot sample,
 it is estimated that 10-20% of the screening sample will be consented. Factoring in a
 conservative dropout estimate of 25-40%, it is more than likely that the proposed study
 sample will be recruited. To account for patient attrition, the study will over-recruit the
 target sample by 5 patients.

 Estimated rates of delirium and neglect after right brain stroke are around 50% . Therefore,
 around 50 of the study sample is expected to have neglect, delirium, or both. In addition to
 using delirium/neglect status as a dichotomous variable, quasi-continuous severity scores for
 each variable will be used, which will increase the power to detect associations between
 these two disorders and brain lesion locations.
Inclusion Criteria:

 - Able to give informed consent prior to beginning of testing; have 1st ever ischemic
 stroke on the right side of the brain; speaks English; >18 years of age

 Exclusion Criteria:

 -  all of the following will be excluded:

 1. Participants with another brain disorder expected to produce severe
 visual-spatial abnormalities (including malignant brain tumor, traumatic brain
 injury with post-injury neurological problems, or Alzheimer's Disease)

 2. Women knowing themselves to be pregnant will not be enrolled; pregnancy may
 itself affect visual-spatial attention. However, women of childbearing potential
 will not be tested for pregnancy before participating.

 3. Persons consuming more than 10 alcoholic beverages weekly

 For participants who will undergo an MRI scan the following will be excluded:

 4. Pacemaker or other implanted electrical device incompatible with the MR
 environment

 5. Eye injury to the eye involving metal filings

 6. Unable to undergo MRI due to doctor recommendation

 7. Claustrophobia