Abstract:
Systems and methods for inter-population assessment of neurobehavioral status employ neurobehavioral profiles to accommodate differing external conditions. Population profiles and external condition data are provided to a neurobehavioral performance model to determine neurobehavioral status under external conditions. Alternatively, neurobehavioral performance values may be retrieved from the profile when such values are stored in conjunction with external condition data. Comparisons of the resulting neurobehavioral status(es) are then determined, and may comprise without limitation one or more of: performance deltas, statistical parameter differences, rankings, above/below performance threshold determinations, pass/fail indicators, and countermeasure recommendations. Populations may comprise pluralities, individuals and empty (“null”) sets. Comparisons may also pertain to one or more relevant times of interest and one or more sets of testing conditions. Fields of application include (without limitation) operational and military fatigue management, medical diagnosis and treatment, fatigue countermeasure training and individualization, sleep research, academic and scientific research, and/or the like.

Description:
RELATED APPLICATIONS 
       [0001]    This application claims the benefit of the priority of U.S. provisional patent application No. 61/508,270 filed 15 Jul. 2011, which is hereby incorporated herein by reference. 
     
    
     TECHNICAL FIELD 
       [0002]    The invention relates to assessing the neurobehavioral status, as identified under a variety of specifiable testing conditions, of a first population of individuals relative to a second population of individuals using neurobehavioral profiles for the first and second populations respectively. Intra-population comparisons facilitate a variety of applications including medical diagnosis and treatment, management of neurobehavioral deficits related to fatigue, individualization of neurobehavioral training regimens, operational and military management, scientific and academic research, and/or the like. 
       BACKGROUND 
       [0003]    Neurobehavioral deficits may be associated with medical conditions, medical disorders, drugs, fatigue, or other factors. For instance fatigue may result from any of a number of factors, including extended wakefulness, night work, shift work, extended duty periods, circadian misalignment, jet lag, or chronic sleep loss. In order to effectively identify, monitor, treat, and/or mitigate neurobehavioral deficits and/or make use of neurobehavioral deficit information one must be able to quantify the degree of neurobehavioral deficits in meaningful terms. Approaches to quantifying neurobehavioral status and the degree of neurobehavioral deficits (also known as “neurobehavioral status”) are required in many applications, such as (without limitation) medical monitoring, medical diagnosis, medical screening, medical treatment, scientific experiments, population-based studies, case studies, fatigue risk management in operational settings, academic, and athletic activities. 
         [0004]    In many operational settings, for instance, it may be difficult to establish fitness-for-duty thresholds for neurobehavioral status of the operator that are expressed in absolute terms of numerical test metrics from an neurobehavioral assessment or numerical results derived from a biomathematical model that estimates neurobehavioral status. 
         [0005]    For instance, readily identifiable neurobehavioral performance standards for particular tasks or assignments are not always articulable with needed accuracy (e.g., the biomathematical model estimates of pilot fatigue levels relative to policy-based limits for safe operation of the aircraft, the maximum number of allowed PVT lapses to operate a commercial motor vehicle, etc.). Nor are the identification of sufficient countermeasures to mitigate neurobehavioral defects when test scores or biomathematical model outputs indicate a neurobehavioral deficit. When the external conditions (e.g., sleep history, countermeasures, environmental factors, etc.) under which a particular task or assignment are to be performed differ from those under which neurobehavioral state was measured or predicted, moreover, the aforementioned difficulties are compounded even further. Therefore, there is a general desire for approaches to compare neurobehavioral status of a given population of (one or more) individuals to a control population of (one or more) individuals, where the neurobehavioral performance of the control population is familiar or readily known. A further need exists for approaches to compare a population to itself under different external conditions, and to compare individuals to themselves and other individuals across different sets of external conditions and at differing time periods of interest. 
       SUMMARY 
       [0006]    One aspect of the invention provides a method employing neurobehavioral profiles with a computer for determining a comparison of the neurobehavioral status of a first population relative to the neurobehavioral status of a second population, the method comprising: receiving, at a computer, a first neurobehavioral profile for a first population, the first neurobehavioral profile indicating a neurobehavioral status of the first population corresponding to a set of testing conditions; receiving, at the computer, a second neurobehavioral profile for a second population, the second neurobehavioral profile of indicating a neurobehavioral status of the second population corresponding to a set of testing conditions; receiving, at the computer, a first set of testing-condition data, the first set of testing-condition data being indicative of a first set of testing conditions; determining, with the computer, a neurobehavioral status for the first population associated with the first set of testing conditions, wherein the neurobehavioral status for the first population associated with the first set of testing conditions is based at least in part on the received first neurobehavioral profile and the received first set of testing-condition data; determining, with the computer, a neurobehavioral status for the second population associated with the first set of testing conditions, wherein the neurobehavioral status for the second population associated with the first set of testing conditions is based at least in part on the received second neurobehavioral profile and the received first set of testing-condition data; and determining, with the computer, a comparison of the determined neurobehavioral status of the first population associated with the first set of testing conditions relative to the determined neurobehavioral status of the second population associated with the first set of testing conditions. 
         [0007]    Another aspect of the invention provides a computer program product embodied in a non-transitory medium and comprising computer-readable instructions that, when executed by a suitable computer, cause the computer to perform a method for determining a comparison of the neurobehavioral status of a first population relative to the neurobehavioral status of a second population, the method comprising: receiving, at a computer, a first neurobehavioral profile for a first population, the first neurobehavioral profile indicating a neurobehavioral status of the first population corresponding to a set of testing conditions; receiving, at the computer, a second neurobehavioral profile for a second population, the second neurobehavioral profile of indicating a neurobehavioral status of the second population corresponding to a set of testing conditions; receiving, at the computer, a first set of testing-condition data, the first set of testing-condition data being indicative of a first set of testing conditions; determining, with the computer, a neurobehavioral status for the first population associated with the first set of testing conditions, wherein the neurobehavioral status for the first population associated with the first set of testing conditions is based at least in part on the received first neurobehavioral profile and the received first set of testing-condition data; determining, with the computer, a neurobehavioral status for the second population associated with the first set of testing conditions, wherein the neurobehavioral status for the second population associated with the first set of testing conditions is based at least in part on the received second neurobehavioral profile and the received first set of testing-condition data; and determining, with the computer, a comparison of the determined neurobehavioral status of the first population associated with the first set of testing conditions relative to the determined neurobehavioral status of the second population associated with the first set of testing conditions. 
         [0008]    Another aspect of the invention provides a system for determining a comparison of the neurobehavioral status of a first population relative to the neurobehavioral status of a second population, the system comprising: a data storage unit, the data storage unit containing a database of neurobehavioral profiles and a database of testing-condition data, and a processor capable of receiving neurobehavioral profiles and testing-condition data from the data storage unit, wherein determining a comparison of the neurobehavioral status of a first population relative to the neurobehavioral status of a second population comprises: receiving, at the computer, a first neurobehavioral profile for a first population, the first neurobehavioral profile being capable of indicating a neurobehavioral status of the first population corresponding to a set of testing conditions; receiving, at the computer, a second neurobehavioral profile for a second population, the second neurobehavioral profile being capable of indicating a neurobehavioral status of the second population corresponding to a set of testing conditions; receiving, at the computer, a first set of testing-condition data, the first set of testing-condition data being indicative of a first set of testing conditions corresponding to a first time of interest; determining, with the computer, a neurobehavioral status for the first population associated with the first set of testing conditions, wherein the neurobehavioral status for the first population associated with the first set of testing conditions is based at least in part on the received first neurobehavioral profile and the received first set of testing-condition data; determining, with the computer, a neurobehavioral status for the second population associated with the first set of testing conditions, wherein the neurobehavioral status for the second population associated with the first set of testing conditions is based at least in part on the received second neurobehavioral profile and the received first set of testing-condition data; and determining, with the computer, a comparison of the determined neurobehavioral status of the first population associated with the first set of testing conditions relative to the determined neurobehavioral status of the second population associated with the first set of testing conditions. 
         [0009]    Further details, features and aspect of particular embodiments are provided in the description below and in the drawings appended hereto. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0010]    Exemplary embodiments are illustrated in referenced figures of the drawings. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than restrictive. 
           [0011]    In drawings which illustrate non-limiting embodiments: 
           [0012]      FIG. 1  is a flowchart for a method  100  for determining a comparison of the neurobehavioral response of a first population relative to the neurobehavioral response of a second population, in accordance with a particular embodiment; 
           [0013]    The multiple views of  FIG. 2  provide exemplary embodiments of a neurobehavioral profile, in accordance particular embodiments of the presently disclosed invention, in which specifically: 
           [0014]      FIG. 2A  is an illustration of a neurobehavioral profile comprising the distributions of an exemplary (and non-limiting) four (4) neurobehavioral traits distributed across a hypothetical population, in accordance with a particular embodiment; 
           [0015]      FIG. 2B  is an illustration of a neurobehavioral profile comprising the distribution of a single neurobehavioral trait across a population, in accordance with a particular embodiment; and 
           [0016]      FIG. 2C  illustrates how the neurobehavioral profile of  FIG. 2B  may be used to provide a comparative assessment of the neurobehavioral state of a hypothetical testing subject, in accordance with a particular embodiment; 
           [0017]      FIG. 2D  is an illustration of a neurobehavioral profile comprising one or more neurobehavioral status values each corresponding to a set of testing conditions, in accordance with a particular embodiment; 
           [0018]    The multiple views of  FIG. 3  provide exemplary embodiments of comparisons of the neurobehavioral status of a first population with respect to a second population comprising an individual, in accordance with particular embodiments of the presently disclosed invention, in which specifically: 
           [0019]      FIG. 3A  is a multi-day graph of the neurobehavioral status of a population receiving eight (8) hours of sleep per day, according to a particular embodiment; 
           [0020]      FIG. 3B  is a multi-day graph of the neurobehavioral status of an individual receiving six (6) hours of sleep per day, according to a particular embodiment; and 
           [0021]      FIG. 3C  is a non-limiting exemplary comparison of the neurobehavioral statuses of the population of  FIG. 3A  and the individual of  FIG. 3B , according to a particular embodiment; 
           [0022]    The multiple views of  FIG. 4  provide exemplary embodiments of comparisons of the neurobehavioral status of a first population comprising a first individual with respect to a second population comprising a second individual, in accordance with particular embodiments of the presently disclosed invention, in which specifically: 
           [0023]      FIG. 4A  is a multi-day graph of the neurobehavioral status of an individual (individual A) receiving eight (8) hours of sleep per day, according to a particular embodiment; and 
           [0024]      FIG. 4B  is a multi-day graph of the neurobehavioral status of an another individual (individual B) receiving eight (8) hours of sleep per day, according to a particular embodiment; 
           [0025]    The multiple views of  FIG. 5  provide exemplary embodiments of comparisons of the neurobehavioral status of a first population with respect to a second population, in accordance with particular embodiments of the presently disclosed invention, in which specifically: 
           [0026]      FIG. 5A  is a multi-day graph of the neurobehavioral status of a population (population A) receiving six (6) hours of sleep per day, according to a particular embodiment; 
           [0027]      FIG. 5B  is a multi-day graph of the neurobehavioral status of another population (population B) receiving six (6) hours of sleep per day, according to a particular embodiment; 
           [0028]      FIG. 5C  is a non-limiting exemplary comparison of the neurobehavioral statuses of the population of  FIG. 5A  (population A) and the population of  FIG. 5B  (population B), according to a particular embodiment; and 
           [0029]      FIG. 5D  is another non-limiting exemplary comparison of the neurobehavioral statuses of the population of  FIG. 5A  (population A) and the population of  FIG. 5B  (population B), according to a particular embodiment; 
           [0030]    The multiple views of  FIG. 6  provide exemplary embodiments of comparisons of the neurobehavioral status of a first population comprising an individual with respect to a second population, in accordance with particular embodiments of the presently disclosed invention, in which specifically: 
           [0031]      FIG. 6A  is a multi-day graph of the neurobehavioral status of an individual receiving seven (7) hours of sleep per day, according to a particular embodiment; 
           [0032]      FIG. 6B  is a multi-day graph of the neurobehavioral status of a population receiving seven (7) hours of sleep per day, according to a particular embodiment; 
           [0033]      FIG. 6C  is a non-limiting exemplary comparison of the neurobehavioral statuses of the individual of  FIG. 6A  and the population of  FIG. 6B , according to a particular embodiment; and 
           [0034]      FIG. 6D  is another non-limiting exemplary comparison of the neurobehavioral statuses of the individual of  FIG. 6A  and the population of  FIG. 6B , according to a particular embodiment; 
           [0035]      FIG. 7  is a block diagram of an exemplary system  700  for carrying out the methods of the presently disclosed invention, in accordance with a particular embodiment; and 
           [0036]      FIG. 8  is a plot showing the variation in the homeostatic process of a typical subject over the transitions between being asleep and being awake, in accordance with particular embodiments. 
       
    
    
     DETAILED DESCRIPTION 
       [0037]    Before the embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangements of the operative components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced or being carried out in various ways. Also, it is understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. The use herein of “including” and “comprising,” and variations thereof, is meant to encompass the items listed thereafter and equivalents thereof. Unless otherwise specifically stated, it is to be understood that steps in the methods described herein can be performed in varying sequences and may be repeated a multiplicity of times in varying orders. 
       Background to Neurobehavioral Performance 
       [0038]    Aspects of the presently disclosed invention relate to particular nuances of neurobehavioral performance. Broadly defined, “neurobehavioral performance” refers to an individual&#39;s ability to perform a specific task that requires one or more cognitive functions that rely on fatigue level and/or fatigue state. Such cognitive functions include (without limitation) concentration, short-term or long-term memory, visual or other sensory acuity, alertness, gross motor dexterity, fine motor skill, and/or the like. As used herein, the terms (used interchangeably) “neurobehavioral performance prediction(s),” “predicted neurobehavioral performance,” and “predicted neurobehavioral performance level(s)” refer to the output of a biomathematical model capable of modeling and/or predicting neurobehavioral performance status when given appropriate inputs. Non-limiting factors that may impact a subject&#39;s neurobehavioral performance include: sleep disruption, sleep restriction, circadian misalignment, sleep inertia, extended task performance, extended work/duty hours, multitasking, (extended) physical exertion, psychological stresses (e.g., time pressure; family, financial, or legal issues etc.), environmental stressors (e.g., extreme temperature or humidity conditions, ambient noise, ambient vibration, ambient light conditions, altitude “hypoxia” etc.), certain medical conditions or behavioral disorders (e.g., Parkinson&#39;s, Alzheimer&#39;s, dementia, or any age-related brain dysfunction or mild cognitive impairment, brain injuries, mood disorders, and certain psychoses, etc.), certain drugs, and/or the like. 
       Methods to Test Neurobehavioral Performance Generally 
       [0039]    The presently disclosed invention may make use of any methods or techniques used to measure neurobehavioral performance. Such methods and techniques may include context-relative performance tasks, such as a workplace-specific task (e.g., assembling X number of specific product units in a particular factory in time T and/or the like), standardized line-of-work specific tasks (e.g., typing a standard document within an acceptable accuracy threshold on standard equipment, and/or the like), and so-called “special tasks” that highlight particular neurobehavioral performance characteristics (e.g., executing a specific complex driving, flying, or navigation maneuver within an acceptable threshold, navigating a standardized obstacle course on foot, assembling a particular standardized complex manufactured object, and/or the like). Performance measures for such neurobehavioral tasks may come from direct human observation, measurement instruments, or from embedded systems (e.g., lane tracking system on a commercial motor vehicle). In medical monitoring, screening, diagnosis and treatment settings neurobehavioral assessment may be made based on physician or medical-care-provider observation or standard instruments used in the field such as (without limitation) the Mini-Mental State Examination (MMSE), the Mini-Cog Test, the Alzheimer&#39;s Disease Assessment Scale-cognitive (ADAS-cog), Ammons Quick Test, National Adult Reading Test (NART), Wechsler Adult Intelligence Scale (WAIS), Wechsler Intelligence Scale for Children (WISC), Wechsler Preschool and Primary Scale of Intelligence (WPPSI), Wechsler Test of Adult Reading (WTAR), California Verbal Learning Test, Cambridge Prospective Memory Test (CAMPROMPT), Doors and People, Memory Assessment Scales (MAS), Rey Auditory Verbal Learning, Test Rivermead Behavioral Memory Test, Test of Memory and Learning (TOMAL), Test of Memory Malingering (TOMM), Wechsler Memory Scale (WMS), Boston Diagnostic Aphasia Examination, Boston Naming Test, Comprehensive Aphasia Test, Lexical Decision Task, Multilingual Aphasia Examination, Behavioral Assessment of Dysexecutive Syndrome (BADS), CogSreen: Aeromedical Edition, Continuous Performance Task (CPT), Controlled Oral Word Association Test (COWAT), d2 Test of Attention, Delis-Kaplan Executive Function System (D-KEFS), Digit Vigilance Test Figural Fluency Test, Halstead Category Test, Halying and Brixton Tests, Iowa Gambling Test, Kaplan Baycrest Neurocognitive Assessment (KBNA), Kaufman Short Neuropsychological Assessment, Paced Auditory Serial Addition Test (PASAT), Pediatric Attention Disorders Diagnostic Screener (PADDS), Ruff Figural Fluency Test, Stroop Task, Test of Variables of Attention (TOVA), Tower of London Test, Trail Making Test (TMT), Trails A &amp; B, Wisconsin Card Sorting task (WCST), Symbol Digit Modalities Test, Clock Test, Hooper Visual Organization Task (VOT), Rey-Osterrieth Complex Figure, Clinical Dementia Rating, Dementia Rating Scale, MCI Screen, Cambridge Neuropsychological Test Automated Battery (CANTB), The Neurobehavioral Cognitive Status Examination (Cognistat), Cognitive Assessment Screening Instrument, CNS Vital Signs (CNSVS), Cognitive Function Scanner (CFS), Dead-Woodcock Neuropsychology Assessment System (DWNAS), General Practitioner Assessment of Cognition (GPCOG), Hooper Visual Organization Test, Luria-Nebraska Neuropsychological Battery, A Developmental Neuropsychological Assessment (NEPSY), Repeatable Battery for the Assessment of Neuropsychological Status, CDR Computerized Assessment System, and/or the like. Furthermore, performance assessment on one or more neurobehavioral tasks may be measured by one or more standard tests including but not limited to: the Psychomotor Vigilance Test (PVT), the Motor Praxis Test (MPraxis), the Visual Object Learning Test (VOLT), the Fractal-2-Back Test (F2B), the Conditional Exclusion Task (CET), the Matrix Reasoning Task (MRsT), the Line Orientation Test (LOT), the Emotion Recognition Task (ER), the Balloon Analog Risk Task (BART), the Digit Symbol Substitution Test (DSST), the Forward Digit Span (FDS), the Reverse Digit Span (BDS), the Serial Addition and Subtraction Task (SAST), the Go/NoGo Task, the Word-Pair Memory Task, the Word Recall Test (Learning, Recall), the Motor Skill Learning Task, the Threat Detect Task, the Descending Subtraction Task (DST), the Positive Affect Negative Affect Scales-Extended version (PANAS-X) Questionnaire, the Pre-Sleep/Post-Sleep Questionnaires for astronauts, the Beck Depression Inventory (BDI), the Conflict Questionnaire, Karolinska Drowsiness Test (KDT), the Visual Analog Scales (VAS), the Karolinska Sleepiness Scale (KSS), the Profile of Mood States Long/Short Form Questionnaire (POMS/POMS SF), the Stroop Test, and/or the like. 
       Methods to Test Fatigue Specifically 
       [0040]    Although the presently disclosed invention may be used generally to compare the neurobehavioral status of one population to that of another, particular embodiments are specifically directed to assessment and comparison of neurobehavioral deficits associated with fatigue. Embodiments of the presently disclosed invention may make use of one or more techniques for measuring or testing an individual&#39;s fatigue levels (referred to hereinafter as “fatigue-measurement techniques”). Particular embodiments of the invention are sufficiently adaptable to utilize many (if not all) of these known fatigue-measurement techniques. Non-limiting and non-mutually exclusive examples of suitable fatigue-measurement techniques which may be used in various embodiments of the invention include testing techniques which use: (i) objective reaction-time tasks, stimulus-response tests, and cognitive tasks such as the Psychomotor Vigilance Task (PVT) or variations thereof (Dinges, D. F. and Powell, J. W. “Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations”  Behavior Research Methods. Instruments . &amp;  Computers  17(6): 652-655, 1985) and/or a so-called digit symbol substitution test; (ii) subjective alertness, sleepiness, or fatigue measures based on questionnaires or scales such as (without limitation) the Stanford Sleepiness Scale, the Epworth Sleepiness Scale (Jons, M. W., “A new method for measuring daytime sleepiness—the Epworth sleepiness scale”  Sleep  14 (6): 54-545, 1991), and the Karolinska Sleepiness Scale (Åkerstedt, T. and Gillberg, M. “Subjective and objective sleepiness in the active individual”  International Journal of Neuroscience  52: 29-37, 1990); (iii) EEG measures and sleep-onset-tests including (without limitation) the Karolinska drowsiness test (Akerstedt, T. and Gillberg, M. “Subjective and objective sleepiness in the active individual”  International Journal of Neuroscience  52: 29-37, 1990), Multiple Sleep Latency Test (MSLT) (Carskadon, M. W. et al., “Guidelines for the multiple sleep latency test—A standard measure of sleepiness”  Sleep  9 (4): 519-524, 1986) and the Maintenance of Wakefulness Test (MWT) (Mitler, M. M., Gujavarty, K. S. and Browman, C. P., “Maintenance of Wakefulness Test: A polysomnographic technique for evaluating treatment efficacy in patients with excessive somnolence”  Electroencephalographv and Clinical Neurophysiology  53:658-661, 1982); (iv) physiological measures such as (without limitation) tests based on blood pressure and heart rate changes, and tests relying on pupillography and/or electrodermal activity (Canisius, S. and Penzel, T., “Vigilance monitoring—review and practical aspects”  Biomedizinische Technik  52(1): 77-82., 2007); (v) embedded performance measurement systems, devices, and processes such as (without limitation) devices that are used to measure a driver&#39;s performance in tracking the lane marker on the road (see, e.g., U.S. Pat. No. 6,894,606); and (vi) simulators that provide a virtual environment to measure specific task proficiency such as commercial airline flight simulators (Neri, D. F., Oyung, R. L., et al., “Controlled breaks as a fatigue countermeasure on the flight deck”  Aviation Space and Environmental Medicine  73(7): 654-664, 2002); and/or (vii) the like. Particular embodiments of the invention may make use of any one or more of the fatigue-measurement techniques described in the aforementioned references or various combinations and/or equivalents thereof. All of the publications referred to in this paragraph are hereby incorporated by reference herein. 
       Models for Predicting Neurobehavioral Performance 
       [0041]    The presently disclosed invention is designed to utilize any biomathematical model designed generally to model any one or more of a human subject&#39;s neurobehavioral performance characteristics. Such biomathematical models are referred to herein as “neurobehavioral performance models.” Particular embodiments are specifically designed to utilize biomathematical models that model a human subject&#39;s fatigue state and/or fatigue-related neurobehavioral deficits levels. Such biomathematical models are referred to herein as “fatigue models.” As used herein, the terms “biomathematical model(s),” “neurobehavioral performance model(s),” and “fatigue model(s)” shall have the following relationship: fatigue models are a subset of neurobehavioral performance models (fatigue being one type of neurobehavioral performance), and neurobehavioral performance models are, in turn, a subset of biomathematical models. 
         [0042]    Among the neurobehavioral performance models utilized by the presently disclosed invention, particular embodiments may utilize the so-called “two-process model” of sleep regulation developed by Borbely et al. in 1999. The Borbely two-process model posits the existence of two primary regulatory mechanisms: (i) a sleep/wake-related mechanism that builds up exponentially during the time that the subject is awake and declines exponentially during the time that the subject is asleep, and is called the “homeostatic process” or “process S;” and (ii) an oscillatory mechanism with a period of (nearly) 24 hours, called the “circadian process” or “process C.” Without wishing to be bound by theory, the circadian process has been demonstrated to be orchestrated by the suprachiasmatic nuclei of the hypothalamus. The neurobiology of the homeostatic process is only partially known and may involve multiple neuroanatomical structures. Total alertness at a given time y(t), which is one non-limiting example of neurobehavioral performance, may then be represented as a sum of the C and S processes (see Equation 3, below). 
         [0043]    Further details related to the application of the Borbely two-process fatigue model are contained in PCT published patent application  Systems and Methods for Individualized Alertness Predictions , inventors Mott C. G., Mollicone, D. J., et al., WIPO publication No. WO 2009/052633, the entirety of which is incorporated herein by reference and from which portions of the following discussion are excerpted for convenience and clarity. Specifically, in accordance with the two-process model, the circadian process C may be represented by: 
         [0000]    
       
         
           
             
               
                 
                   
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         [0000]    where t denotes clock time (in hours, e.g. relative to midnight), φ represents the circadian phase offset (i.e. the timing of the circadian process C relative to clock time), γ represents the circadian amplitude, and τ represents the circadian period which may be fixed at a value of approximately or exactly 24 hours. The summation over the index/serves to allow for harmonics in the sinusoidal shape of the circadian process. For one particular application of the two-process model for alertness prediction, l has been taken to vary from 1 to 5, with constants a 1  being fixed at a 1 =0.97, a 2 =0.22, a 3 =0.07, a 4 =0.03, and a 5 =0.001. 
         [0044]    The homeostatic process S may be represented by: 
         [0000]    
       
         
           
             
               
                 
                   
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         [0000]    (S&gt;0), where t denotes (cumulative) clock time, Δt represents the duration of time step from a previously calculated value of S, ρ w  represents the time constant for the build-up of the homeostatic process during wakefulness, and ρ s  represents the time constant for the recovery of the homeostatic process during sleep. 
         [0045]    Given equations (1), (2a) and (2b), the total alertness according to the two-process model may be expressed as a sum of: the circadian process C, the homeostatic process S multiplied by a scaling factor κ, and an added noise component ε(t): 
         [0000]        y ( t )= KS ( t )+ C ( t )+ε( t )  (3)
 
         [0046]    Furthermore, it is useful to be able to describe the homeostatic process S for test subject after one or more transitions between being asleep and being awake. The sleep-wake transitions are commonly (but without limitation) represented as square wave signals oscillating between the binary states of being asleep (value=1 herein, without limitation) and being awake (value=0 herein, without limitation), referred to as sleep functions. Other mathematical representations of sleep status and effectiveness can be utilized by the presently disclosed invention. 
         [0047]    As described in more particular detail below, the systems and methods of the invention may make use of measured neurobehavioral performance levels that are typically only available when the subject is awake. Consequently, it may be desirable to describe the homeostatic process between successive periods that the test subject is awake. As the circadian process C is independent from the homeostatic process  5 , we may consider as an illustrative case of neurobehavioral performance using only the homeostatic process S of equations (2a), (2b). Consider the period between t 0  and t 3  shown in  FIG. 8 . During this period, the subject undergoes a transition from awake to asleep at time t 1  and a transition from asleep to awake at time t 2 . Applying the homeostatic equations (2a), (2b) to the individual segments of the period between t 0  and t 3  yields: 
         [0000]        S ( t   1 )= S ( t   0 ) e   −p     e     T     1   +(1 −e   −p     e     T     1   )  (4a)
 
         [0000]        S ( t   2 )= S ( t   1 ) e   −p     e     T     2     (4b)
 
         [0000]        S ( t   3 )= S ( t   2 ) e   −p     e     T     3   +(1 −e   −p     e     T     3   )  (4c)
 
         [0000]      where 
         [0000]        T   1   =t   1   −t   0   (5a)
 
         [0000]        T   2   =t   2   −t   1   (5b)
 
         [0000]        T   3   =t   3   −t   2   (5c)
 
         [0048]    Substituting equation (5a) into (5b) and then (5b) into (5c) yields an equation for the homeostat at a time t 3  as a function of an initial known homeostat condition S(t 0 ), the time constants of the homeostatic equations (ρ w , ρ s ) and the transition durations (T 1 , T 2 , T 3 ): 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         
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         [0049]    Equation (6) applies to the circumstance where t 0  occurs during a period when the test subject is awake, there is a single transition between awake and asleep at t 1  (where t 0 &lt;t 1 &lt;t 3 ), there is a single transition between asleep and awake at t 2  (where t 1 &lt;t 2 &lt;t 3 ), and then t 3  occurs after the test subject is awake again. 
         [0050]    Additional fatigue models may be utilized by particular embodiments. Other non-limiting examples of fatigue models include Akerstedt&#39;s “three-process model of alertness” (see, e.g., Akerstadt, T., et al. “Predictions from the Three-Process Model of Alertness,”  Aviation, Space, and Environmental Medicine,  75:No. 3, §II (March 2004); see also Akerstedt, T. et al. “A Model of Human Sleepiness,” excerpted from  Sleep &#39; 90 J. Horne, Ed. (Pontenagel Press 1990)); Achermann&#39;s “two-process model revisited” (see e.g., Achermann, P., “The Two-Process Model of Sleep Regulation Revisited,”  Aviation, Space, and Environmental Medicine,  75:No. 3, §II (March 2004)); Avinash&#39;s “process-U model” (see Avinash, D., “Parameter Estimation for a Biomathematical Model of Psychomotor Vigilance Performance under Laboratory Conditions of Chronic Sleep,”  Sleep - Wake Research in the Netherlands  16:39-42 (Dutch Society for Sleep-Wake Research 2005); Beersma&#39;s “modified two-process model” (see, e.g., Beersma, D. G. M., “Models of Human Sleep Regulation,”  Sleep Medicine Reviews  2:No. 1, pp. 31-43 (W.B. Saunders Co. Ltd. 1998)); Belyavin and Spencer&#39;s “QinetiQ Approach” (see, e.g., Belyavin, A. J. and Spencer, M. B., “Modeling Performance and Alertness: the QinetiQ Approach,”  Aviation, Space, and Environmental Medicine,  75:No. 3, §II (March 2004)); the “circadian alertness simulator” (see, e.g., Dijk, D. J., et al. “Fatigue and Performance Models: General Background and Commentary on the Circadian Alertness Simulator for Fatigue Risk Assessment in Transportation,”  Aviation, Space, and Environmental Medicine,  75:No. 3, §II (March 2004)); the so-called “new model class” (see, e.g., McCauley, P., et al, “A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance,”  Journal of Theoretical Biology,  256:227-239 (Reed-Elsevier 2009)); alternative models such as nonparametric approaches and neural networks (see, e.g., Reifman, J., “Alternative Methods for Modeling Fatigue and Performance,”  Aviation, Space, and Environmental Medicine,  75:No. 3, §II (March 2004)); and/or the like. Particular embodiments of the presently disclosed invention may make use of any one or more of the biomathematical models described in the aforementioned references or various combinations and/or equivalents thereof. All of the publications referred to in this paragraph are hereby incorporated by reference herein. 
         [0051]    The presently disclosed invention may utilize one or more of the foregoing biomathematical models to predict neurobehavioral performance levels when certain inputs are provided. Particular embodiments may focus on fatigue as the specific type of neurobehavioral status being measured and/or assessed. 
         [0052]    Embodiments of the invention use fatigue models and/or their model parameters to estimate trait values for fatigue-related individual traits which may not be directly measurable or observable. As used in this description and the accompanying claims, the word “trait” is used to refer to a characteristic of a particular individual subject that have enduring (i.e. relatively non-time-varying) values for the individual subject. Traits differ as between individual subjects. Non-limiting examples of fatigue-related individual traits for a subject include: whether the subject is alert on a minimum amount of sleep; whether the subject is a “night owl” (i.e. relatively more alert late at night) or a “morning person” (i.e. relatively more alert in the early morning); the rate of fatigue level increase for the subject during wakefulness (e.g. the rate of homeostatic buildup (ρ w )); the rate of fatigue level reduction for the subject during sleep (e.g. the rate of homeostatic recovery (ρ s ); the extent to which time of day (circadian rhythm) influences alertness for the subject (e.g. circadian amplitude (γ)); aptitude for specific performance tasks for the subject; other traits for the subject described in Van Dongen et al., 2005 (Van Dongen et al., “Individual difference in adult human sleep and wakefulness: Leitmotif for a research agenda.”  Sleep  28 (4): 479-496, 2005), which are hereby incorporated herein by reference. 
         [0053]    An individual&#39;s traits may be contrasted with the individual&#39;s “states”. As used in this description and the accompanying claims, the word “state” is used to describe characteristics of a particular individual which vary with time and which may one or more circumstances or external conditions (e.g. sleep history, light exposure, etc.). Non-limiting examples of individual states of a subject include: the amount of sleep that the subject had in the immediately preceding day(s); the level of homeostatic process of the subject at the present time; the circadian phase of the subject (Czeisler, C., Dijk, D, Duffy, J., “Entrained phase of the circadian pacemaker serves to stabilize alertness and performance throughout the habitual waking day,”  Sleep Onset: Normal and Abnormal Processes , pp. 89-110, 1994 (“Czeisler, C. et al.”)); the current value of light response sensitivity in the circadian process (Czeisler, C., Dijk, D, Duffy, J., “Entrained phase of the circadian pacemaker serves to stabilize alertness and performance throughout the habitual waking day,” pp. 89-110, 1994); the levels of hormones for the subject such as cortisol, or melatonin, etc. (Vgontzas, A. N., Zoumakis, E., et al., “Adverse effects of modest sleep restriction on sleepiness, performance, and inflammatory cytokines.”  Journal of Clinical Endocrinology and Metabolism  89(5): 2119-2126, 2004); the levels of pharmacological agent(s) for the subject known to affect alertness such as caffeine, or Modafinil (Kamimori, G. H., Johnson, D., et al., “Multiple caffeine doses maintain vigilance during early morning operations.”  Aviation Space and Environmental Medicine  76(11): 1046-1030, 2005). The references referred to in this paragraph are hereby incorporated herein by reference. 
         [0054]    In this description and the accompanying claims, the term “individual” (or “subject,” used synonymously) is used to refer a person from whom neurobehavioral performance data is collected and concerning whom a comparison of a neurobehavioral status to some other individual or population is sought. (As used herein populations may comprise single individuals.) Conversely, in this description and the accompanying claims, the term “user” is used to refer to a person from whom data is collected for whom the outputted comparison of neurobehavioral statuses between two populations is determined. “User” may refer to a person or organization that may be supervising the operation of the methods and systems described herein and that may make use of the compared neurobehavioral statuses about the subject individual(s) or population(s). By way of non-limiting example: users may comprise corporate or sole employers who may have an interest in monitoring, educating or improving the performance of subjects who may be employees; users may comprise military officers or commanders who may have an interest in overseeing military units which may include groups of subjects; users may include one or more researchers who may want to collect research data to test populations of subjects; and/or the like. 
         [0055]    In this description and the accompanying claims, the term “population” is used to refer to a set of individuals (typically, although not exclusively, a set human beings) from whom data is collected and about whom the neurobehavioral profile is tailored. A used in this description and the accompanying claims, a “population” may comprise a single individual, or it may comprise no individuals (i.e., is the “null set”). 
       The Figures 
       [0056]      FIG. 1  provides a flowchart for method  100  used to determine the comparison  1000  of the neurobehavioral status of a first population  802  to the neurobehavioral status of a second population  902 , in accordance with particular non-limiting embodiments. Method  100  commences in steps  101  and  102 , wherein a first neurobehavioral profile  801  and a second neurobehavioral profile  901  are received, respectively. First neurobehavioral profile  801  is capable of indicating a neurobehavioral status of the first population  802  when matched to a set of testing conditions. According to particular non-limiting embodiments, first population  802  may comprise an experimental population. Second neurobehavioral profile  901  is also capable of indicating a neurobehavioral status of the second population  902  when matched to a set of testing conditions. According to particular non-limiting embodiments, second population  902  may comprise a control population. According to particular embodiments populations  802 ,  902  are arbitrary populations. According to particular embodiments populations  802 ,  902  may comprise one or more of a workforce, a military unit, a plurality of individuals with shared demographics, a plurality of individuals with one or more shared medical conditions, and/or the like. In this description and the accompanying claims, the term “population” may comprise a single individual or may be empty (i.e., comprising the null set). According to particular embodiments populations  802 ,  902  may each comprise single individuals, may both comprise the same individual, and may either or both comprise the null set. Furthermore, according to some embodiments, first population  802  may comprise a member of second population  902 , and according to other embodiments second population  902  may comprise a member of first population  802 . According to particular embodiments, more than two populations (up to an arbitrary number N) may be used for comparison purposes through repeated application of the methods disclosed herein in appropriate combinations. 
         [0057]    In this description and the accompanying claims, the term “neurobehavioral profile” is used to refer to either a set of one or more neurobehavioral trait values corresponding to a population (see, e.g.,  FIG. 2A  and surrounding discussion) or a set of one or more neurobehavioral performance values each corresponding to particular testing conditions (see, e.g.,  FIG. 2D  and surrounding discussion). A neurobehavioral profile, such as first neurobehavioral profile  801  and second neurobehavioral profile  901 , may be created by the collection of multiple neurobehavioral performance assessments across a wide range of (testing) conditions. In particular embodiments, the neurobehavioral profile consists simply of the neurobehavioral performance values and matching external conditions. Such embodiments may take the form of a list, a database, an array, a table, a look-up table, a hashtable, and/or the like. In other embodiments, neurobehavioral profiles  801 ,  901  are created through the aforementioned collection of neurobehavioral performance data but also comprise applying a neurobehavioral performance model to the data to determine values for the set of one or more neurobehavioral traits that comprise the profile. In such embodiments, the trait values themselves comprise the profile, and neurobehavioral performance can be estimated using the traits by applying a neurobehavioral performance under an assumed or provided set of conditions. In particular embodiments, collection of neurobehavioral performance data may occur across a sufficiently diverse set of conditions such that the profile is not biased toward a particular set of conditions. In other embodiments, such condition biases may be created through the careful selection of neurobehavioral performance data associated with particular conditions. (Further details regarding testing conditions are provided, below, in connection with the discussion of step  103  of method  100 A.) In other embodiments neurobehavioral profiles  801 ,  901  may be created through the aggregation of measured neurobehavioral status measurements (or performance measurements) under known testing conditions. 
         [0058]    According to some embodiments, particular trait-based neurobehavioral profiles may be model dependent—i.e., the set of one or more neurobehavioral traits that comprise a neurobehavioral profile are commonly (though not necessarily) tied to a specific neurobehavioral performance model. Particular embodiments utilize neurobehavioral profiles that depend upon the two-state model of fatigue prediction (see, e.g., Borbley 1999). Of the embodiments that utilize neurobehavioral profiles that depend upon the two-state model of fatigue predictions, some embodiments utilize sets of neurobehavioral traits that comprise one or more of: circadian phase offset φ, circadian phase amplitude γ, circadian period τ, one or more Fourier constants a 1  for harmonics in the sinusoidal shape of the circadian process, the time constant ρ w  for the rate of homeostatic buildup during wakefulness, the time constant ρ s  for the rate of homeostatic recovery during sleep, the arbitrary scaling factor κ, a noise coefficient ε or function ε(t), and/or the like. Other embodiments may utilize neurobehavioral profiles that depend upon one or more of the three-process model of alertness, the two-process model revisited, the process-U model, the modified two-process model, the QinetiQ approach, the circadian alertness simulator, alternative models such as nonparametric approaches and neural networks, and/or the like. The presently disclosed invention may utilize neurobehavioral profiles that depend upon any neurobehavioral performance models known in the art and that are comprised of sets of any neurobehavioral traits known in the art. 
         [0059]    The multiple views of  FIG. 2  illustrate (non-limiting) exemplary embodiments of neurobehavioral profiles according to the presently disclosed invention.  FIG. 2A , for example, illustrates a neurobehavioral profile comprising four (4) distributions of distinct neurobehavioral trait values  201 ,  202 ,  203 ,  204  as exhibited in a hypothetical population. Trait-value distributions  201 ,  202 ,  203 ,  204  may be distributions of any neurobehavioral trait known in the art and may optionally be associated with any one or more neurobehavioral performance models known in the art. In particular embodiments (not shown), a neurobehavioral profile of the variety shown in  FIG. 2A  may be constructed for an individual instead of a plurality of individuals comprising a population. In such embodiments, the neurobehavioral profile will not comprise distributions of trait values, but rather single values for each trait (with optional error ranges, error bars, and/or error distributions according to the measurement and data-collection techniques used to gather the trait values). 
         [0060]    To determine a neurobehavioral status using a neurobehavioral profile of the variety illustrated in  FIG. 2A , one must specify a set of testing conditions and then supply a neurobehavioral performance model. Applying the model to the traits and testing conditions will then result in a neurobehavioral performance estimate. 
         [0061]    Distributions  201 ,  202 ,  203 ,  204  are illustrated as near perfect normal distributions by way of example, but this idealized condition need not be the case for all embodiments. In the case of neurobehavioral profiles comprising large data sets of neurobehavioral traits (e.g., a large number of performance assessments conducted on a large number of individuals within a population), idealized normal distributions may be expected, but when data sets on neurobehavioral traits are smaller (e.g., fewer assessments on only a small number of individuals), deviations from perfect normalized distributions may occur. Furthermore a neurobehavioral profile according to the presently disclosed invention may comprise an arbitrary number of distributions of neurobehavioral traits for the corresponding population. 
         [0062]      FIG. 2D  comprises a table or array of neurobehavioral performance values  210 -A through  210 -G, each corresponding to a set of known testing conditions,  211 -A through  211 -G, respectively. (Testing conditions  211 -A through  211 -G comprise four fields of data each, corresponding to the respective data fields labeled “Condition 1 ,” “Condition 2 ,” “Condition 3 ,” and “Condition 4 .”) According to particular embodiments, the neurobehavioral performance values  210 -A through  210 -G are neurobehavioral performance values that were actually measured with respect to testing subject  110  when the known testing conditions  211 -A through  211 -G were present, respectively. In the hypothetical neurobehavioral profile of  FIG. 2D , the neurobehavioral performance values indicated are the number of lapses in a 3-minute PVT, and the testing conditions  211 -A through  211 -G illustrated comprise prior 3-day sleep history (Condition 1 ), amount of caffeine ingested in past three (3) hours (Condition 2 ), the time at which the test was administered (Condition 3 ), and the severity of a common medical condition (Condition 4 ). (“AH 1 ” represents the apnea-hypopnea index for individuals suffering from sleep apnea or other sleep-disordered breathing condition, measured as the number of cessations in breathing lasting ten seconds or longer per one hour of sleep.) 
         [0063]    To determine a neurobehavioral status using a neurobehavioral profile of the variety illustrated in  FIG. 2D , one may specify a set of testing conditions and then search for the specified testing conditions within the one or more set of testing conditions comprising the profile and return the neurobehavioral performance associated therewith. Various search algorithms may be implanted to accomplish this task. By way of example, a search algorithm may comprise first calculating a numeric distance function between the specified testing condition and another test condition based on a weighted sum of the absolute difference between corresponding test condition values, then determining the testing condition that has the lowest numeric distance to the specified testing condition. 
         [0064]      FIG. 2B  provides another example of a neurobehavioral profile (of the  FIG. 2A  variety) according to the present invention, namely, a single-trait profile comprising a solitary distribution for a PVT metric (e.g., number of lapses, mean reaction time, fastest ten-percent reaction time, etc.) across a hypothetical population. The PVT metric may optionally be associated with one or more neurobehavioral performance models known in the art according to some embodiments. According to other embodiments, the PVT metric may be associated with the two-state model of alertness prediction. 
         [0065]    Method  100  continues in step  103 , in which a first set of testing-condition data  805  is received. First set of testing-condition data  805  reflects a particular set of testing conditions  804  under which a neurobehavioral status of first population  802  is desired for comparison purposes. The first set of testing conditions  804  may also be associated with a first time of interest for when the neurobehavioral status of the first population  802  may be desired. A first time of interest may comprise one or more of: reporting for work, reporting for military duty, undergoing medical examination, undergoing medical treatment, driving a vehicle, operating machinery, physical activity, athletic competition, enrolling in the military from civilian life, resuming civilian life after military duty, engaging in a task with an associated neurobehavioral or fatigue risk, and/or the like. 
         [0066]    In this description and the accompanying claims, the term “testing condition” (used synonymously with “external condition” or simply “condition,”) is used to refer to one or more variables, factors, conditions, or inputs that may impact the measurement of a subject&#39;s neurobehavioral performance (other than the neurobehavioral status itself) during a neurobehavioral performance assessment. Such variables may be analyzed into the following non-limiting list of categories: sleep and work history (comprising any factors related to an individual&#39;s or a populations sleep and work states), so-called “external factors” (relating to environmental conditions that may affect results of neurobehavioral performance assessments), dosing or application of neurobehavioral countermeasures (such as stimulants and additional sleep), and presence of neurobehavioral stressors (specific factors known to impact neurobehavioral performance). Specific types of data within each category include the following non-limiting list of examples: i) sleep and work history: actigraphy, a sleep schedule, one or more sleep onset times, one or more sleep interval durations, a duration of total time in bed over an extended period, a work schedule, one or more work shift identifiers, one or more work start times, one or more work interval durations, and a duration of total work time over an extended interval; ii) external factors: weather data, environmental data, and noise or sound data; iii) dosing or application of neurobehavioral countermeasures: a schedule of stimulant ingestion, a sleep schedule, a schedule of physical activity, and an exercise schedule; and iv) existence of neurobehavioral stressors: prolonged wakefulness, circadian misalignment, extended time on duty, and night work. 
         [0067]    Method  100  continues in step  104 , in which a neurobehavioral status  806 - 1  of the first population  802  corresponding to the first set of testing conditions is determined. Neurobehavioral status  806 - 1  corresponds to the neurobehavioral status of first population  802  as would be exhibited under the first set of testing conditions  804  indicated by the step- 103  received first set of testing-condition data  805 . Neurobehavioral status  806 - 1  is determined either by applying a neurobehavioral performance model to the neurobehavioral trait parameters identified in first neurobehavioral profile  801  subject to the first set of target testing conditions  804  or by locating in the neurobehavioral profile  801  the neurobehavioral performance values associated with the testing conditions indicated by the step- 103  received set of testing-condition data. In particular embodiments, the neurobehavioral performance model used to determine the first neurobehavioral status  806 - 1  is the same neurobehavioral performance model associated with the first neurobehavioral profile  801 . In other embodiments, different neurobehavioral performance models may be used. 
         [0068]    By way of non-limiting example,  FIG. 2C  illustrates how the single-trait profile of  FIG. 2B  may be used along with a step- 103  received first set of testing-condition data  805  to determine a step- 104  determined neurobehavioral status  806 - 1 , in accordance with particular embodiments. The neurobehavioral profile of  FIGS. 2B and 2C  comprises a solitary distribution of a PVT metric across a hypothetical population. Two PVT scores are identified for a specific individual in  FIG. 2C . Score  206  corresponds to the individual&#39;s base score (e.g., the PVT score he or she received upon being tested while reporting for work or military duty). Score  207  corresponds to the individual&#39;s predicted score. The predicted score, according to particular embodiments, corresponds to the score the individual (or population) might expect to receive if tested under a different set of external conditions. Score  207  is predicted by a neurobehavioral performance model associated with the neurobehavioral profile of  FIGS. 2B and 2C  in light of step- 103  received first set of testing-condition data  805 . 
         [0069]    Method  100  continues in step  105 , in which a neurobehavioral status  806 - 2  is determined for second population  902 . Neurobehavioral status  806 - 2  corresponds to the neurobehavioral status of the second population  902  as would be exhibited under the first set of testing conditions  804  indicated by the step- 103  received first set of testing-condition data  805 . Neurobehavioral status  806 - 2  is analogous in all ways to neurobehavioral status  806 - 1 , except that neurobehavioral status  806 - 2  pertains to the second population  901 . 
         [0070]    Method  100  may continue in optional step  106 , in which a second set of testing-condition data  905  is received. Second set of testing-condition data  905  reflects a particular second set of testing conditions  904  under which a neurobehavioral status of either the first population  801  or the second population  901  may be desired for comparison purposes. Second set of testing conditions  904  may be associated with a second time of interest in a fashion similar to that of the first set of testing conditions  804  discussed in connection with step  103 . Second time of interest may comprise any one or more of the stated times discussed therewith. Second time of interest may optionally be the same time or a comparable time to the first time of interest, in accordance with particular embodiments. 
         [0071]    Method  100  may then continue in optional step  107 , in which a neurobehavioral status  906 - 1  is determined for first population  802 . Neurobehavioral status  906 - 1  corresponds to the neurobehavioral status of first population  802  as would be exhibited under the second set of testing conditions  904  indicated by the optional step- 105  received second set of test-condition data  905 . Neurobehavioral status  906 - 1  is determined in an analogous fashion (and is in all ways otherwise analogous) to neurobehavioral status  806 - 1 , except that neurobehavioral status  906 - 1  pertains to the step- 106  received set of second testing-condition data  905 . 
         [0072]    Method  100  may then continue in optional step  108 , in which a neurobehavioral status  906 - 2  is determined for second population  902 . Neurobehavioral status  906 - 2  corresponds to the neurobehavioral status of second population  902  as would be exhibited under the second set of testing conditions  904  indicated by the optional step- 105  received second set of test-condition data  905 . Neurobehavioral status  906 - 2  is determined in an analogous fashion (and is in all ways otherwise analogous) to neurobehavioral status  806 - 2 , except that neurobehavioral status  906 - 1  pertains to the step- 106  received set of second testing-condition data  905 . 
         [0073]    Method  100  continues in step  109 , in which a comparison  1000  of the neurobehavioral status  806 - 1  of the first population  802  associated with the first set of testing conditions  804  is determined with respect to the neurobehavioral status  806 - 2  of the second population  902  associated with the first set of testing conditions  804 . A step- 109  comparison  1000  may take any of several forms, as discussed below in connection with the multiple views of  FIG. 3  through the multiple views of  FIG. 6 . For the introductory sample case of  FIG. 2C , one particular step- 107  determined comparison  1000  may comprise the “region of improvement” between the base score  206  and the predicted score  207 , which may be represented as one or more of a difference in scores, a difference in numerical rank among the population, a difference in percentile raking among the population, a percentage change, whether a threshold score (not shown) was exceeded, and/or the like. 
         [0074]    Method  100  may also continue with optional step  110  in which case additional comparisons  1010  may be determined. According to particular embodiments additional comparisons  1010  may involve comparing neurobehavioral status of either the first population  802  or the second population  902  across different sets of testing conditions  804 ,  904 . According to other embodiments, additional comparisons  1010  may involve comparing the neurobehavioral status of the first population to the neurobehavioral status of the second population, but in accordance with the second set of testing conditions. 
         [0075]    In particular embodiments, method  100  is executed a single time and in the order of steps presented, although such restrictions are not an essential component of the present invention. In other embodiments one or more steps may be repeated, or the steps may be executed out of order. In particular embodiments, steps  103  (receive first set of testing conditions  84 ),  104  (determine first neurobehavioral status  806 ), and  107  (determine comparison  1000 ) may be repeated an arbitrary number of times so that a plurality of comparisons  1000  may be determined in step  108  for a plurality of different first sets of testing-condition data  805 . Additionally, in particular embodiments, steps  102  (receive second neurobehavioral profile),  103 ,  104 , and  107  may be repeated a plurality of times so that a plurality of comparisons  1000  may be determined in step  108  for different second populations  902 . Similarly, any sequence of steps in method  100  may be repeated so as to create a plurality of comparisons  1000  in step  108  that leads to similar comparisons with one or more variables, data sets, or inputs changed. 
         [0076]    The multiple views of  FIG. 3  provide non-limiting examples of step- 107  determined comparisons  1000  of the neurobehavioral status  806  of the first population  802  to the neurobehavioral status  906  of the second population  902 , in accordance with particular embodiments, wherein the second population  902  comprises an individual. Specifically,  FIG. 3A  provides a multi-day chart illustrating the neurobehavioral status (e.g., fatigue state) of a first population  802 . The neurobehavioral status of first population  802  is illustrated as a set of three (3) distinct neurobehavioral status graphs  301 ,  302 ,  303 . Neurobehavioral status graphs  301  and  302  represent the “outer boundaries” (i.e., performance assessment scores of the highest and the lowest scoring individuals within the population) of the neurobehavioral status of first population  802  over the time frame indicated. Neurobehavioral status graph  303  represents an average or mean neurobehavioral performance of first population  802 . 
         [0077]      FIG. 3B  provides a corresponding multi-day chart for second population  902 , wherein second population  902  comprises an individual. Neurobehavioral status graph  304  therefore represents the neurobehavioral status of the individual comprising second population  902  over the time frame indicated. It must be noted that for a proper comparison  1000  of the first determined neurobehavioral status  806  to the second neurobehavioral status  906  to be conducted in step  107  of method  100 , the difference in sleep history conditions between first population  802  (8 hours per day) and second population  902  (5 hours per day) must be accounted for. This can be accomplished by appropriate selection of one or more of the first or second received sets of testing-condition data  805 ,  905  in steps  103  and  106  of method  100 , respectively. This could be accomplished by setting the received set of first testing-condition data  805  to include a 5-hour sleep schedule in step  103 , or it could be accomplished by setting the optional received set of second testing condition data  905  to include an 8-hour sleep schedule in optional step  105 . The neurobehavioral performance model associated with the received first and second neurobehavioral profiles  801 ,  901  would be able to convert neurobehavioral performance and/or neurobehavioral status values from one sleep schedule to the other. 
         [0078]    Regarding respective neurobehavioral statuses  806 ,  906  of populations  802  and  902 ,  FIG. 3C  provides one non-limiting way in which to determine the comparison  1000  in step  107  of method  100 . Graph  305  is a histogram of the neurobehavioral status of all members of first population  802 . Boundary  306  represents the neurobehavioral status of the individual comprising second population  902 . A display report might be given in which a percentage ranking is shown (e.g., “The individual  902  is better off than 90% of the population  802 .”). Other non-limiting examples of comparisons  1000  between first and second populations  802  and  902 , wherein second population  902  comprises an individual include: a percentile raking of the individual with respect to the second population, a numerical ranking of the individual with respect to the second population, a percentage of the second population with neurobehavioral response above or below the neurobehavioral status of the individual, the number of members of the second population with neurobehavioral status above or below the neurobehavioral response of the individual, and/or the like. 
         [0079]    The multiple views of  FIG. 4  provide non-limiting examples of step- 107  determined comparisons  1000  of the neurobehavioral status  806  of the first population  802  to the neurobehavioral status  906  of the second population  902 , in accordance with particular embodiments, wherein the both the first and the second population  902  comprise individuals (whether the same or different individuals). Specifically,  FIG. 4A  provides a multi-day chart illustrating the neurobehavioral performance (e.g., fatigue state) of a first population  802  comprising an individual. An overall neurobehavioral status  402  corresponding to the entire time interval of interest (i.e., an average neurobehavioral status value of 4.55) is also shown. 
         [0080]      FIG. 4B  provides a multi-day chart illustrating the neurobehavioral performance  403  of a second population  902 . An overall neurobehavioral performance status  404  corresponding to the time interval of interest (e.g., an average neurobehavioral status value of 4.34) is also shown. Non-limiting examples of comparisons  1000  between the neurobehavioral statuses  806 ,  906  of first and second populations  802  and  902 , wherein both first and second populations  802 ,  902  comprise an individual include: a difference in neurobehavioral status under differing first and second set of testing conditions, a difference in neurobehavioral status under differing first and second time periods of interest, a percentage change in neurobehavioral status under differing first and second set of testing conditions, a percentage change in neurobehavioral status under differing first and second time periods of interest, a recommended countermeasure to improve neurobehavioral performance to a particular threshold, and/or the like. 
         [0081]    The multiple views of  FIG. 5  provide non-limiting examples of step- 107  determined comparisons  1000  of the neurobehavioral status  806  of the first population  802  to the neurobehavioral status  906  of the second population  902 , in accordance with particular embodiments, wherein both the first and the second populations  802 ,  902  comprises populations (whether the same or different populations). Specifically,  FIG. 5A  provides a multi-day chart illustrating the neurobehavioral status (e.g., fatigue state) of a first population  802 . The neurobehavioral status of first population  802  is illustrated as a set of three (3) distinct neurobehavioral status graph  501 ,  502 ,  503 . Neurobehavioral status graphs  501  and  502  represent the outer boundaries of the neurobehavioral status of first population  802  over the time frame indicated. Neurobehavioral status graph  503  represents an average or mean neurobehavioral performance of first population  802 . 
         [0082]    Similarly,  FIG. 5B  provides a multi-day chart illustrating the neurobehavioral performance of a second population  902 . The neurobehavioral status of second population  902  is illustrated as a set of three (3) distinct neurobehavioral status graph  504 ,  505 ,  506 . Neurobehavioral status graphs  504  and  505  represent the outer boundaries of the neurobehavioral status of second population  902  over the time frame indicated. Neurobehavioral status graph  506  represents an average or mean neurobehavioral performance of second population  902 . 
         [0083]    Regarding respective neurobehavioral statuses  806 ,  906  of populations  802  and  902 ,  FIG. 5C  provides a non-limiting way in which to determine the comparison  1000  in step  107  of method  100 . Graphs  507  and  508  are histograms of neurobehavioral performance scores for each member of first population  802  and second population  902 , respectively. Boundary  509  represents an arbitrary threshold (perhaps dictated by operational objectives, industry or legal standards, or mere custom). A display report might be given in which a percentage above or below threshold  902  may be indicated. 
         [0084]    Another non-limiting comparison  1000  is shown in  FIG. 5D . Graphs  510  and  511  are cumulative distribution functions for first and second populations  802 ,  902 , respectively, indicating the percentage of each population below a particular neurobehavioral status level. Arbitrary boundary  509  is also illustrated. A display report might be given in which a percentage of each population  802 ,  902  above or below threshold  509  may be indicated, such as display reports  512 ,  513  respectively. Other non-limiting examples of comparisons  1000  between first and second populations  802  and  902 , wherein second population  902  comprises an individual include: a percentage of the first population with a neurobehavioral status above or below the neurobehavioral status of the individual, a number of individuals within the first population with a neurobehavioral status above or below the neurobehavioral status of the individual, a ratio of the number of individuals within the first population with a neurobehavioral status above the neurobehavioral status of the individual to the number of individuals within the first population with a neurobehavioral status below the neurobehavioral status of the individual, and/or the like. 
         [0085]    The multiple views of  FIG. 6  provide non-limiting examples of step- 107  determined comparisons  1000  of the neurobehavioral status  806  of the first population  802  to the neurobehavioral status  906  of the second population  902 , in accordance with particular embodiments, wherein the first population  802  comprises an individual. Specifically,  FIG. 6A  provides a corresponding multi-day chart for first population  802 , wherein first population  802  comprises an individual. Neurobehavioral status graph  601  therefore represents the neurobehavioral status of the individual comprising first population  802  over the time frame indicated. 
         [0086]      FIG. 6B  provides a multi-day chart illustrating the neurobehavioral performance of a second population  902 . The neurobehavioral status of second population  902  is illustrated as a set of three (3) distinct neurobehavioral status graph  602 ,  603 ,  604 . Neurobehavioral status graphs  602  and  603  represent the outer boundaries of the neurobehavioral status of second population  902  over the time frame indicated. Neurobehavioral status graph  604  represents an average or mean neurobehavioral performance of second population  902 . 
         [0087]    Regarding respective neurobehavioral statuses  806 ,  906  of populations  802  and  902 ,  FIG. 6C  provides one non-limiting way in which to conduct the comparison  1000  in step  107  of method  100 .  FIG. 6C  is a histogram  605  of neurobehavioral performance scores for each member of second population  902 . The neurobehavioral performance score  606  for the individual comprising first population  802  is illustrated as well. A display report might be provided in which a ranking of the individual comprising first population  802  may be given with respect to the neurobehavioral performance distribution of second population  902 . 
         [0088]    Another non-limiting comparison  1000  is shown in  FIG. 6D . Graph  608  is a ranking of all members of second populations  902  according to their neurobehavioral status level. The neurobehavioral status level  608  of the individual comprising first population  802  is also depicted and corresponds to the neurobehavioral status level  609  of the individual. A display report  611  might be provided in which a numerical ranking of the individual comprising first population  802  may be given with respect to the neurobehavioral performance distribution of second population  902  (e.g., 77th of 100, as shown). 
         [0089]    Additional comparisons  1010  may be determined in any of the same or similar fashions as comparisons  1000  are determined and as illustrated in the foregoing multiple views of  FIG. 3  through the multiple views of  FIG. 6  (and associated discussion herein). Additional comparisons  1010  differ from comparisons  1000  only in that additional comparisons  1010  comprise a comparison of the neurobehavioral status one or more of the first and second population under the first set of testing conditions with the neurobehavioral status of one or more of the first and second populations under the second set of testing conditions. In all other respects, additional comparisons  1010  are the same as comparisons  1000 . 
         [0090]    Particular embodiments of the invention may be implemented using suitably configured computer systems.  FIG. 7  shows a schematic illustration of a system  700  for determining a comparison of first and second neurobehavioral statuses, according to a particular, non-limiting, embodiment. The illustrated system  700  comprises: data storage  701 , a computer or computer network  702  (e.g. any device with suitable processing capacity and I/O capabilities, including networked computers, intranets, the Internet, mobile computing platforms, embedded devices, etc.), input device  703 , and a display  704 . In some implementations, some of these components may be the components that make up a personal computer, a mobile phone, personal media player, or any other device that contains the four aforementioned basic components. Data storage  701  may optionally contain neurobehavioral profiles  705  (optionally organized into a database, as shown), testing-condition data  706 , a population selector  707  for associating specific populations to particular neurobehavioral profiles, and system software  708  (not shown). System software  708 , when executed by computer  702 , can cause computer  702  to perform the methods described herein. Neurobehavioral profiles  705 , testing-condition data  706 , and population selector  707  for associating specific populations to particular neurobehavioral profiles can all be utilized by computer  702  when performing such methods. Neurobehavioral profiles  705  may optionally comprise neurobehavioral performance models (as described herein). 
         [0091]    Certain implementations of the invention may be used in medical diagnosis and/or medical treatment. Medical diagnostic embodiments may comprise assigning the patient to first population  802  and a reference healthy population to second population  902 . The reference population may share one or more demographic or health-related characteristics in common with the patient, and comparisons of neurobehavioral performance may then be able to detect substantial deviations from reference population norms. Continued comparisons to the reference population throughout the monitoring, screening, diagnosis, or treatment phase of medical care may also be facilitated by the presently disclosed invention. 
         [0092]    Certain implementations may also focus on the individualization of a countermeasure training regimen. Use of countermeasures constitutes an external condition under certain embodiments (see, e.g.,  FIG. 2D ). By repeated application of the methods for comparing neurobehavioral performance among a countermeasure-maximizing subject and either him-/herself or a reference population, finding optimized countermeasure strategies (e.g., precise stimulant dosage) for varying external conditions may be found. Such applications can be of particular use for military during training, deployment and post-deployment when personnel are readjusting to civilian life where stimulant overuse and/or addiction may exist. For instance, the presently disclosed invention may assist such individuals taper their stimulant consumption while maintaining an acceptable neurobehavioral performance relative to a population-based standard (e.g., performance of their troop or platoon, performance of other military personnel with similar demographics, etc.). 
         [0093]    Certain implementations of the invention comprise computer processors which execute software instructions which cause the processors to perform a method of the invention. For example, one or more processors may implement data processing steps in the methods described herein by executing software instructions retrieved from a program memory accessible to the processors. The invention may also be provided in the form of a program product. The program product may comprise any medium which carries a set of computer-readable instructions which, when executed by a data processor, cause the data processor to execute a method of the invention. Program products according to the invention may be in any of a wide variety of forms. The program product may comprise, for example, physical media such as magnetic data storage media including floppy diskettes, hard disk drives, optical data storage media including CD ROMs and DVDs, electronic data storage media including ROMs, flash RAM, or the like. The instructions may be present on the program product in encrypted and/or compressed formats. 
         [0094]    Where a component (e.g. a software module, processor, assembly, device, circuit, etc.) is referred to above, unless otherwise indicated, reference to that component (including a reference to a “means”) should be interpreted as including as equivalents of that component any component which performs the function of the described component (i.e. that is functionally equivalent), including components which are not structurally equivalent to the disclosed structure which performs the function in the illustrated exemplary embodiments of the invention. 
         [0095]    As will be apparent to those skilled in the art in light of the foregoing disclosure, many alterations and modifications are possible in the practice of this invention without departing from the spirit or scope thereof. 
         [0096]    While a number of exemplary aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, additions and sub-combinations thereof. It is therefore intended that the following appended claims and claims hereafter introduced are interpreted to include all such modifications, permutations, additions and sub-combinations as are within their true spirit and scope.