FAQ
What number and type of subjects are
used in the database of asymptomatic
control (AC) scans in NeuroQ?
What number and type of subjects are used in the database of
asymptomatic control (AC) scans in NeuroQ?
The databases used in the development of NeuroQ were culled from clinical (n=600) and research (n=200) scans used in the study of
patients with varying degrees of pre-dementia or dementia,
patients matched for cognitive symptoms but without progressive dementing conditions as proved by long-term follow-up,
clinical patients without neuropsychiatric symptoms (i.e., true normals, in the statistical sense of normal distribution of the neuropsychiatrically healthy population), and
people recruited as research controls following extensive neuropsychologic screening (i.e., normal as documented by research criteria).
The AC scans employed in the currently released version of NeuroQ come from 50 normal subjects representing an approximately equal mix of males and females drawn from groups 3 and 4 noted above across a broad age spectrum (20-89 yrs). It was validated in this context that following the internal normalization and regional weighting routines used in NeuroQ and associated research tools, there were no group, sex, or age-related effects on values of brain regions that altered the accuracy of predictions for Alzheimer’s or other neurodegenerative dementias. Diagnostic and prognostic accuracies were found to be equal or better to those achieved by dedicated region of interest analysis and by expert visual analysis (Silverman et al., J Nucl Med. 2001, 42:224P; Silverman et al., J Nucl Med. 2003, 44:17P; Silverman et al., J Nucl Med. 2004, 45.)
Clarify why age segmentation isn't
a part of the NeuroQ application.
Clarify why age segmentation isn't a part of the NeuroQ
application.
When performing research studies comparing one group of subjects to another, stratification for age (and other demographic criteria) can be very valuable, in maintaining well-matched experimental and control groups. In comparing individual patients for clinical purposes, on the other hand -- where the goal is not to say how one group or condition compares to another, but rather what the metabolic images show in this individual's brain -- additional considerations pertain, and adjustments made for age can be quite misleading. For example, some individual healthy 70 year olds have brains with atrophy-related changes typical for someone 55 years old, while other healthy 70 year olds have brains with atrophy-related changes typical for someone 85 years old (this spectrum is something that is seen frequently.) In the context of the NeuroQ package, age stratification is not only unnecessary, but would actually be undesirable, for a number of reasons.
First, several published studies have shown (as reviewed in Radiologic Clinics of North America, 2005; 43:67-77) that in healthy aging, patterns of cerebral metabolism and perfusion demonstrate very little systematic change with age, and the one change that is consistently reported (in anteromedial prefrontal cortex) is distinct from changes that occur in the context of Alzheimer's and other neurodegenerative diseases.
Second, we have designed the NeuroQ display to visually represent that which is actually apparent in the primary PET scan data, and not give output that is distorted by diminished-sample statistical drifts, or models or assumptions about effects of aging. For example, when it is visually obvious that the medial prefrontal cortex is substantially decreased in the cerebral metabolism of an 80-year old, we want this to be reflected in the Program's color display rather than have the output appear all blue (as could occur if the output were age-adjusted). Moreover, since our normal database spans a wide age spectrum (20-89 years old), regions affected most by normal aging will have correspondingly larger SD's of normal variation, and this provides a built-in stabilization of the statistical component reflected in our two-dimensional (magnitude and statistical differences from the mean) color tool.
Third, since getting older is the single most powerful non-genetic predisposing factor for dementia, any attempt to age-adjust the database runs the risk of 'adjusting out' important information contained in the original scan. Except when the absence of early dementia-related changes are definitively proven (as rarely occurs) by autopsy (or very long-term clinical follow-up) of the 'normal' older patients, age adjustments may actually obscure metabolic data that are neurologically meaningful.
What is the justification for the
normal versus abnormal cutoff
threshold of 1.65 SD?
What is the justification for the normal versus abnormal
cutoff threshold of 1.65 SD?
Just as the conventional +/- 2 SD's represents the 95% inclusion interval from the mean in a normal distribution for a two-tailed comparison, 1.65 SD's represents the equivalent threshold for a one-tailed comparison (with dementia and most other neuropathologic conditions, we can pre-specify whether we are searching for hypometabolism or hypermetabolism in a given area.) That is, regions with metabolism falling more than 1.65 SD's below the mean (color-coded in the "hypometabolism" display) represent the lowest 5% of a normal distribution; regions with metabolism falling more than 1.65 SD's above the mean (color-coded in the "hypermetabolism" display) represent the highest 5% of a normal distribution.
Is NeuroQ the only application a physician needs to complete his
interpretation of the patient’s brain
PET study?
Is NeuroQ the only application a physician needs to complete his
interpretation of the patient’s brain PET study?
NeuroQ provides display of both the patients transaxial image data sets along with quantitative analysis output displays providing the physician with a comprehensive package for interpretation of the patients brain PET study. The transaxial image display screen provides contrast enhancement tools and an option to change the color translation table. There are no other display tools required in order to complete the interpretation of the study.
Is there intent to add to the
baseline data for the reference
or sampling group over time?
Is there intent to add to the baseline data for the reference
or sampling group over time?
Not at the present time.We do have a program that allows the user to add to the UCLA database, however, that is awaiting further FDA review. The user must be aware that if they alter the normal database that is provided with the NeuroQ application then the accuracy results published for the NeuroQ application would no longer be applicable.ce or sampling group over time?
Why do we need to leave in multiple
options for iterations if 10 is the one
recommended? Is there anytime when
you would want to use less than 10?
Why do we need to leave in multiple options for iterations if 10 is the one
recommended? Is there anytime when you would want to use
less than 10?
For the most standard use of the Program (i.e., comparing an individual scan from the end-user's site to NeuroQ's built-in database), it is recommended to use 10 iterations in conjunction with the 3 QC routines (Axial Plane Limits, De-Scalp, Rigid Registration) , and this is the only option that normally will ever need to be selected. The other choices in the pull-down menu have been retained in order to allow flexibility to use the tool in other ways: for example,
rapid testing of software operability by selecting a small number of iterations,
processing a batch of scans acquired at the end-user's site to create a new database with whatever number of iterations is desired,
sequential use of the reiteration routine, such as adding another 5 iterations to a scan that had already been re-iterated 10 times in order to obtain a closer spatial fit in a case where the individual's original scan is structurally very different from the normal template scan,
various research applications, etc...
In the normalization process, when
would you want to normalize to Pons,
Cerebellum, Sensorimotor Cortex,
Thalamus, or Whole Body.
In the normalization process, when would you want to
normalize to Pons, Cerebellum, Sensorimotor Cortex, Thalamus,
or Whole Body.
The general answer to this question is that one should use reference regions that are known or expected to be unaffected (or least affected) by the disease of interest, when the areas of affected brain become too widespread to make the whole-brain default option a sensitive normalization denominator. Pons is a good choice for cortical dementias such as Alzheimer's disease. Cerebellum may prove particularly useful when assessing FDG patterns in patients with diffuse forebrain involvement such as bilateral cerebrovascular insults in the carotid distributions, or assessing certain other tracers, such as uptake of F-DOPA into basal ganglia. In addition, this feature allows the use of reference regions which are known to be comparably affected to other regions of the brain by conditions which are present but unrelated to diseases of interest -- for example, using sensorimotor cortex in a patient with globally decreased cortical metabolism secondary to sedation or widespread sulcal widening with atrophy.
Is tumor localization a part of the
NeuroQ program?
Is tumor localization a part of the NeuroQ program?
We would not anticipate tumor localization being a common use of this kind of software, which is much more valuable for simultaneously assessing the integrity of metabolism in dozens of neurologic structural (e.g., cortical gyri, thalamus, cerebellum) and neurologic functional (e.g., Broca's area) units, as is needed in evaluation of patients for neuropsychiatric symptoms.
The purpose of “reformatting” the original patient data is to transform the original patient data into the template space of the template data. The 240 standardized ROI’s and resultant 47 3D clusters used for the quantitative analysis in NeuroQ were drawn on the template data. In order to compare a patient to this template and use these sROI’s and clusters, the patient data must first be transformed into this template space. This is accomplished in 2 parts in NeuroQ, first, a rigid alignment is performed that transforms the patient data into the same general orientation as the template data, then a 10-iteration elastic “reformatting” is performed that transforms the patient data to be the same size and shape of the template data. Because the reformatting is an elastic process, it may stretch and/or contract regions of the brain causing them to look blurry. This is a normal consequence of this process and is necessary so that the shapes of the standardized regions become a reasonable match between the template and the patient data.
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