Current and Future Assessment, Identification, and

Current and Future Assessment, Identification,

and Intervention: Going from the Model T to the Tesla

(Part 2)

Paul Beljan, PsyD, ABPdN, ABNJustin Gardner, PsyDDustin Howard, PsyD

Acknowledgements & DeclarationsCenter for Clinical Systems Biology, Rochester General HospitalGordon Broderick, PhD

Cole Lyman, MS

Beljan Psychological ServicesPaul Beljan, PsyD, ABPdN, ABN

Midwestern University, GlendaleJessica Powell, PsyDThomas Virden, III, PhD

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors have no business affiliations and are not using this presentation to promote any products.

The opinions and assertions contained herein are the private views of the author and are not to be construed as official or as reflecting the views of Beljan Psychological Services, Midwestern University, Glendale, or Rochester Regional Health 2

Flow of Material

3

TreatmentComputational ModelingAutismNeuropsychology

Pediatric Neuropsychologythe state of the field1

Working with the Model T

5

Diagnosis

Using normative data and clinical judgment to identify a disorder

TreatmentProviding a list of potential treatment recommendations

02

01

Bringing out the Tesla

6

DiagnosisUsing normative data, clinical judgment, and

process analysis to identify a disorder

TreatmentProviding tailoredtreatment recommendations for maximal outcomes

Predictive ModelingMaximizing diagnostic accuracy and refining individualized neuropsychological dynamics

03

01 02

MultidisciplinaryDrawing on knowledge from different disciplines, each staying within their own boundaries

Integrating Specialties

TransdisciplinaryIntegrating the disciplines in a context that transcends each one’s traditional boundaries

7Choi & Pak, 2006

Autism Spectrum Disordercomputational modeling in context2

1/59Children in the US are diagnosed with ASD

$11.5 trillionLifetime social cost by 2029 given exponential increase in incidence

$3.6 millionLifetime social cost per person

9Baoi et al., 2018

Autism Throughout History

10

1 3 5

42

Leo Kanner (1943)

Hans Asperger (1944)

DSM-IVAutism

Asperger’s

Childhood Disintegrative D/O

PDD-NOS

Transdisciplinary

Approaches

Childhood

Psychopathy

DSM-5

Spectrum (Continuum)

ASD & the DSM-5⬢ Persistent deficits in

social communication and social interaction⬡ Social-emotional

reciprocity⬡ Non-verbal

communication⬡ Developing relationships

⬢ Restricted, repetitive patterns of behavior, interests, or activities⬡ Stereotypy⬡ Insistence on sameness⬡ Restricted and fixated

interests⬡ Hyper- or hypo-

reactivity to sensory input

11APA, 2013

Severity⬢ Level 1:

RequiringSupport

⬢ Level 2: RequiringSubstantial Support

⬢ Level 3: Requiring Very Substantial Support

12APA, 2013

“Neurotypical” vs “Neurodivergent”Call it what it is – a disorder

But Wait, There’s More!DSM-5 nosology of ASD is a good place to start, but it is not the end-all be-all

Clinicians must utilize expertise and deductive reasoning (and maybe

some math) to fill in the rest

13

Diagnostic MethodsObservationGARS-3

Poor SensitivityFair Specificity

ADI-RHigh SensitivityPoor Specificity

ADOS-2Adequate AccuracyDecent inter-Rater Reliability

NeuropsychologyNEPSY-II

Low Theory of Mind scoreLow Affect Recognition score

<WISC-V><Low FSIQ score?><Higher PIQ than VIQ?>Bx Observations and Process

MedicineGenetics

NRXNNLGNSHANK

NeuroanatomyDLPFCCerebellumLocalized overconnectivity

14Carlson et al., 2017

Zander et al., 2015

McCrimmon et al., 2014

Woodbury-Smith & Sherer, 2018)

Floris et al., 2018

Dawson et al., 2007

Brooks et al., 2010

Treatment ModalitiesMedicationAtypical Antipsychotics (irritability)

Stimulants (inattention)

Alpha2A-Adrenergics (inattention)

Serotonergics (emotional/behavioral)

*Used only to manage secondary symptoms of ASD

TherapyApplied Behavior Analysis (ABA) to shape adaptive and social behaviors

Psychotherapy to treat social/emotional sequelae

Social Skills Training to emulate non-ASD social skills

*The most research-backed treatment modalities to remediate ASD-like

behaviors

15

“Early intervention is only half

the battle

16

Issues with the Model T⬢ Vaguely defined

constructs⬡ i.e., “Reciprocity”?

⬡ Low focus on the process of how constructs are produced in the brain

⬢ Disjointed measures⬡ GARS/ADI-R/ADOS

⬢ Poor disciplinary overlap⬡ SLP

⬡ Psychology

⬡ Medicine

⬢ Variable diagnostic accuracies⬡ AUC

⬡ Reliability

⬢ Poorly defined quanta⬡ “Spectrum” = Dartboard!

⬢ Generalized treatment recommendations⬡ “See a psychiatrist”

⬡ “See a therapist”

⬢ Non-time dependent course of treatment⬡ “Do these whenever

and however”

17

Computational Modelingthe Tesla3

“There are likely to be new advances in assessment

technology, but not assessment philosophy

19

Harvey, 2012

Bridging the GapPsychologists should integrate advanced mathematics, graph theory, and data visualization in research and clinical practice

⬡ More accurate diagnostics⬡ More individualized treatment recommendations⬡ Greater ability to track treatment progress

20Parsons & Duffield, 2020

Computational Modeling MethodsBlack Box Models

Probabilistic outcomes are based solely on the nature of inputs (i.e., machine learning)

*High Interpolation*Sensitive to data used

White Box ModelsHow the internal structure of systems affect the identification of an output, based on 1st principles

*High extrapolation*Good with sparse data

21

First Principles Models

A basic proposition that starts directly at the level

of established laws*Deductive Reasoning*

22

Mechanistic Modeling and ASD: The AimsTo develop a mechanistic model of neuropsychological

regulation with the capacity for identifying and explaining the neuropsychological impairment of individuals with ASD, informing clinical diagnostics, and predicting dynamic

treatment recommendations tailored for maximal restoration of neuropsychological functioning

23

LET’S REVIEW SOME CONCEPTSMechanicsA system in which a function exists that transforms inputs into outputs through deterministic methods

DynamicsA mechansitic system in which a function describes the time dependence of variables

RegulationThe mechanistic interaction that explains dynamical shifts in the homeostasis of a network

24

Maximal OutcomesThe most efficient path of attaining the best possible treatment outcomes given the initial condition and available treatments

Principles of Regulatory ModelingA. System processes occur as part of a highly integrated closed-loop network of elements which interact in specific logical manners to produce complex behaviors

B. Network models are robust and have built-in redundancies and complementary functions to compensate for disturbances

C. Human mechanistic regulatory network models can describe illness dynamics with varying levels of granularity

25Broderick & Craddock, 2013

26

27

Healthy Resting

Chronic illness

Healthy Resting

Chronic illness

Chronic illness

Healthy Resting

Non-Illness PerturbationGenetics

EnvironmentTeratogens

Illness

Homeostatics

28

Closed loop systems are adherent to a relatively stable homeostatic fluctuation unless perturbed by a significant outside force

Healthy Resting

Chronic illness

Healthy Resting

Chronic illness

Chronic illness

Healthy Resting

Non-ASD Perturbation ASD

Autism Homeostatics

How to Get There

29

Network Model

CreationAttractor

LandscapeNetwork

Model Validation

Develop a circuit diagram of neuropsychological regulation using text-mining and domain expertise with specified constraints

Ground model & compare it against sample data to determine fidelity of hypothesis-driven network

Assess the resultant attractor landscape to determine allowable homeostatic states

30

Nodes:

A

B

C

D

Edges:

A upregulates BA upregulates CB upregulates DB downregulates CC downregulates DD downregulates A

A B C D

GAD 0 1 -1 1

MDD 1 -1 0 -1

Parameterization

1 = up-regulated (high)

0 = nominal (average)

-1 = down-regulated (low)

Hypothetical Network CreationNote: Such formalism

allows you to approximate

dynamics without detailed

kinetic information

31

HPG Axis Network Creation

Nodes:Hypothalamus

GnRH

LH

FSH

Testosterone

Edges:Hypothalamus → GnRHGnRH → LHGnRH → FSHLH → TestosteroneFSH → TestosteroneTestosterone → FSHTestosterone → LHTestosterone → Hypothalamus

Hypothalamus

GnRH

FSH

Testosterone

LH

Hypo GnRH LH FSH Test

GAD 0 1 -1 1 -1

MDD 1 -1 0 -1 1

Parameterization

1 = up-regulated (high)

0 = nominal (average)

-1 = down-regulated (low)

32Gardner et al., 2021

Psychoneuroimmunology of PTSD

Sleep

Neurotransmitters

Brain Regions

Cytokines

Psych

Physiology

Neuropsych

Neuropsychological Functioning for Symptoms of ASD?

33

AIMS

34

TREATMENT

STRUCTURE

DIAGNOSTICS

DYNAMICS

AUTISM

Is there a model of neuropsychological regulation?Can the model explain dynamic relaxation trajectories?Can the model predict a stable attractor space coinciding with ASD?

Network Model Creation⬢ Neuropsychological Nodes⬢ Neuropsychological Regulations⬢ Prior Knowledge Support

35

36

Neuropsychological Domains Model Nodes

Intelligence Intelligence Quotient

Attention Attention

Working Memory

Executive Functioning Inhibition

Perseveration

Learning/Memory Learning

Memory

Facial Recognition

Language Expressive Language

Receptive Language

Auditory Processing

Academics Mathematics

Reading

Visual-Motor Visual-Spatial Processing

Motor Skills

Social Social Skills

Social Withdrawal

Theory of Mind

Emotion Identification

Behavioral Aggression

Atypicality

Echolalia

Hyperactivity

Self-Injurious Behavior

Stereotypy

Psychological Anxiety

Depression

Emotional Lability

Hypersensitivity

Biological Head Size

Pupillary Distance

Neuropsychological Nodes

31 total variables (“nodes”)

Text Mining

37Retrieved from https://www.Elsevier.com

38

Neuropsychological RegulationsEdges175 total edges

88 upregulations

87 downregulations

In-DegreeEach node is regulated by 5 othersM=5.6; SD=3.75

Each node is upregulated by 3 others M=2.84; SD=2.2

Each construct is down-regulated by 3 others M=2.8; SD=2.4

Out-DegreeEach node regulates 6 others M=5.7; SD=4.4

Each node upregulates 3 othersM=2.8; SD=2.2

Each node down-regulates 3 othersM=2.8; SD=2.9

39

Prior Knowledge SupportTotal Network4,246 Citations

Edge Means = 24(34.2)

Node Means = 136.9(166.4)

By Edge TypesDirect Regulation = 1,167 (M=33.3)

Quant. Change = 908 (M=18.5)

Expression = 2,191 (M=24.1)

By Node TypesNeuro -> Bx = 36 edges (855 cit.; M=24)

Neuro -> Neuro = 47 edges (633 cit.; M=13)

Bx -> Neuro = 38 edges (1,135 cit.; M=29)

Bx -> Bx = 42 edges (1,561 cit.; M=37)

Bio -> Neuro = 7 edges (87 cit., M=12)

Bio -> Bx = 3 edges (3 cit.; M=1)

40

Network StructureFindings

Network Diameter: shortest distance between two most distant nodes = 5

Average Number of Neighboring Nodes: average number of nodes within one “jump” = 10.9

Connection Density: the ratio between the total number of edges compared to total possible number of edges = 0.19

InterpretationThe model is relatively dense with a high number of interactions

Constructs in neuropsychology are highly affected by changes in other domains

Changes in one neuropsychological domain cause rapid and widespreadchanges across all neuropsychological domains

41

Network MechanicsFindings

Closeness: how quickly information spreads from a given node to all other nodesBetweenness: the relative control a node has over the interaction of others (“bridge”)Authorities: those with high in-degreesHubs: those with high out-degrees

InterpretationAnxiety, Atypicality, and Depression cause the largest network-wide regulatory impactDepression, Anxiety, Attention, and Atypicality are the most common intermediaries (“bridges”)Attention, Self-Injurious Behavior, and Learning are highly dependent on changes in othersMotor Skills, Atypicality, and Perseveration cause the largest changes in others

42

Key Mechanistic Finding Results suggest the most important measures to assess when determining overall neuropsychological functioning in ASD are:

Attention Motor SkillsLearning AtypicalityPerseveration Self-Injurious Behavior

43

AIMS

44

TREATMENT

STRUCTURE

DIAGNOSTICS

DYNAMICS

AUTISM


Network Validation⬢ How well does the model explain clinical cases?

45

46

Node Level DesignationsAggression 0 = average; 1 = highAnxiety 0 = average; 1 = highAttention 0 = very low; 1 = low; 2 = averageAtypicality 0 = average; 1 = highAuditory Processing 0 = very low; 1 = averageDepression 0 = average; 1 = highEcholalia 0 = absent; 1 = presentEmotion Identification 0 = very low; 1 = low; 2 = averageEmotional Lability 0 = average; 1 = highExpressive Language 0 = low; 1 = averageFacial Recognition 0 = low; 1 = averageHead Size 0 = average; 1= largeHyperactivity 0 = average; 1 = highHypersensitivity 0 = average; 1 = highInhibition 0 = very low; 1 = low; 2 = averageIntelligence 0 = low; 1 = averageLearning 0 = low; 1 = averageMathematics 0 = very low; 1 = low; 2 = averageMemory 0 = low; 1 = averageMotor Skills 0 = low; 1 = averagePerseveration 0 = average; 1 = high; 2 = very highPupillary Distance 0 = average; 1 = wideReading 0 = very low; 1 = low; 2 = averageReceptive Language 0 = low; 1 = averageSelf-Injurious Behavior 0 = absent; 1 = presentSocial Skills 0 = very low; 1 = low; 2 = averageSocial Withdrawal 0 = average; 1 = highStereotypy 0 = absent; 1 = presentTheory of Mind 0 = very low; 1 = low; 2 = averageVisual Spatial Processing 0 = low; 1 = averageWorking Memory 0 = low; 1 = average

Logic

al Pa

ram

eter

s

Node LFE HFE Health Level Designations

Aggression 1 0 0 0 = average; 1 = highAnxiety 1 1 0 0 = average; 1 = highAttention 0 1 2 0 = very low; 1 = low; 2 = averageAtypicality 1 1 0 0 = average; 1 = highAuditory Processing 0 0 1 0 = very low; 1 = averageDepression 1 0 0 0 = average; 1 = highEcholalia 1 0 0 0 = absent; 1 = presentEmotion Identification 0 1 2 0 = very low; 1 = low; 2 = averageEmotional Lability 1 1 0 0 = average; 1 = highExpressive Language 0 1 1 0 = low; 1 = averageFacial Recognition 0 0 1 0 = low; 1 = averageHead Size 1 1 0 0 = average; 1= largeHyperactivity 1 0 0 0 = average; 1 = highHypersensitivity 1 1 0 0 = average; 1 = highInhibition 0 1 2 0 = very low; 1 = low; 2 = averageIntelligence 0 1 1 0 = low; 1 = averageLearning 0 1 1 0 = low; 1 = averageMathematics 0 1 2 0 = very low; 1 = low; 2 = averageMemory 0 1 1 0 = low; 1 = averageMotor Skills 0 0 1 0 = low; 1 = averagePerseveration 2 1 0 0 = average; 1 = high; 2 = very highPupillary Distance 1 1 0 0 = average; 1 = wideReading 0 1 2 0 = very low; 1 = low; 2 = averageReceptive Language 0 1 1 0 = low; 1 = averageSelf-Injurious Behavior 1 0 0 0 = absent; 1 = presentSocial Skills 0 1 2 0 = very low; 1 = low; 2 = averageSocial Withdrawal 1 1 0 0 = average; 1 = highStereotypy 1 1 0 0 = absent; 1 = presentTheory of Mind 0 1 2 0 = very low; 1 = low; 2 = averageVisual Spatial Processing 0 1 1 0 = low; 1 = averageWorking Memory 0 1 1 0 = low; 1 = average

Mode

l Con

strain

ts

Source of Validation Sets

48

Node Published

Pseudonym

Source

LF1 Case 2 Kanner, 1943; 1971







LF8 Hellmuth Asperger, 1944

HF1 Case 1 Kanner, 1943; 1971




HF5 Fritz Asperger, 1944

HF6 Harro Asperger, 1944

HF7 Ernst Asperger, 1944

Note:The model needs to be constrained by clinical cases of given disease states so it will adhere to observed pathological topologies

Validation Set Findings⬢ Low-Functioning ASD N=8⬢ High-Functioning ASD N=7

49

Note – The model is not

built on the 15 cases,

only validated on them. It

was built on the samples

used in over 4,000

research articles!

50

The discrepancy between the individual measures across the neuropsychological profiles of the sample data (LF1-8 and HF1-7) to the mechanistic expert-defined model (LFE and HFE)

51

Percent error in trajectory between LFE and each LF sample

&

Percent error in trajectory between HFE and each HF sample.

ValidationFindings

⬢ Highest nodal error = perseveration (2.5%) and ToM(1.9%)

⬢ Most nodal errors were below 0.75%

⬢ Classification accuracy was 72.2%⬡ Not just of diagnosis, but

neuropsychological profiles of scores across all domains

Interpretation⬢ The model explains the

clinically observed fluctuations of functioning across time and environments

⬢ The model is highly reliable

⬢ The model has a high degree of sensitivity and specificity, and is much better with LF

52

AIMS

53

TREATMENT

STRUCTURE

DIAGNOSTICS

DYNAMICS

AUTISM


Attractor Landscaping⬢ How discrepant is the neuropsychological

functioning of ASD from non-ASD?

54

55

Attractor Spaces.Think: Einstein

56

57

Healthy Resting

Chronic illness

Healthy Resting

Chronic illness

Chronic illness

Healthy Resting

Non-ASD ASD

Autism Landscaping

Manhattan Distances

58

DA1 DA2 DA3 LFE HFE Health

DA1 0 26 5 17 26 26

DA2 26 0 12 39 18 0

DA3 5 21 0 20 23 21

LFE 17 39 20 0 21 39

HFE 26 18 23 21 0 18

Health 26 0 21 39 18 0

Note: the

maximum MD

is 40 bits

Attractor LandscapingFindings

⬢ Attractor search returned 3 undefined stable discovered attractors (DA1, DA2, DA3)

⬢ The most proximal to DA1 was LF (MD = 17)

⬢ The most proximal to DA2 was Health (MD = 0)

⬢ DA3 was too distant from HF

Interpretation⬢ The model can predict a set of

neuropsychological attractors proximal to health and low-functioning ASD

⬢ The model perfectly explains healthy neuropsychological functioning

⬢ The model explains a condition very similar to low-functioning ASD

59

Treatmentproviding the most efficient recommendations4

FUTURE AIMS

61

TREATMENT

STRUCTURE

DIAGNOSTICS

DYNAMICS

AUTISM

Is there a model of neuropsychological regulation?Can the model explain dynamic relaxation trajectories?Can the model predict a stable attractor space coinciding with ASD?Is there a mechanistic network model of ASD?Can the model inform individualized treatment recommendations?

Iterative Edge Cutting

62

Nodes:A

B

C

D

Edges:

A upregulates B

A upregulates C

B upregulates D

B downregulates C

C downregulates D

D downregulates A

Rice et al., 2016

Which to Cut/Re-Polarize?

63

DA1 LFE

DA1 0 0

LFE 0 0

DA3 HFE

DA3 0 0

HFE 0 0

LF ASD

HF ASD

64

Neuropsychological Emulation

FUTURE AIMS

65

TREATMENT

STRUCTURE

DIAGNOSTICS

DYNAMICS

AUTISM

Is there a model of neuropsychological regulation?

Can the model explain dynamic relaxation trajectories?

Can the model predict a stable attractor space coinciding with ASD?

Is there a mechanistic network model of ASD?

Can the model inform individualized treatment recommendations?

66

Healthy Resting

Chronic injury

Healthy Resting

Chronic injury

Chronic injury

Healthy Resting

Healthy Resting

Chronic injury

Healthy Resting

Chronic injury

Chronic injury

Healthy Resting

Therapeutic remodeling

Think: “Post-Traumatic Growth”

Monte Carlo Simulations⬢ The use of combinatory optimization

schemes to identify individualized treatments and their time course for maximal remittance of symptoms

67Craddock et al., 2015

Standard Treatment for ASD

68

Social

SkillsAnxiety ToM Motor Skills Attention

What’s a better way?

Most Effective Treatment Aims for ASD

69

Social

SkillsAnxiety ToM Motor Skills Attention

Anxiety ToM Motor Skills Attention

What’s an even better way?

Treatment Time Course

70

Attention Anxiety Motor Skills ToM

Anxiety ToM Motor Skills Attention

What’s an EVEN better way?

Compounding Complexity

71

Attention

TxRx

Rx 1 Rx 2 Tx 1 Tx 2

Anxiety Motor Skills ToM

The Benefit of Computational Modeling⬢ Merging clinical expertise with simple

deductive reasoning to enhance diagnostics and take treatment to the next level

⬢ All you need is the right tools! (and a bit of math)

72

Key Take-Awayslet’s sum it all up5

Key Take-Aways⬢ There is much untapped text-based clinical data in

published research, and text-mining can allow clinicians to synthesize a large amount of useful information

⬢ Most of what we know about the neuropsychological profile of ASD is within behavioral correlations (anxiety, depression, social skills, etc.), with little emphasis placed on neuropsychological or biological causatives

74

Key Take-Aways 2⬢ The proposed mechanistic model of neuropsychological

functioning demonstrated a 72% diagnostic accuracy⬢ The model perfectly explains healthy neuropsychological

functioning

75

Key Take-Aways 3⬢ The same “unbroken” neuropsychological circuitry explains

low-functioning ASD with a high degree of fidelity⬢ The underlying neuropsychological circuitry in ASD is not as

impaired as we think!⬢ Next step is to iteratively cut wires to ascertain a model that

perfectly explains ASD, thereby knowing which nodes and edges are unalterably affected

76

Key Take-Aways 4⬢ It may be possible to computationally derive treatment

methods for maximal emulation of typical neuropsychological functioning in children with ASD

⬢ There is a coming shift in which clinicians will begin moving away from traditional statistics into process-based predictive modeling

⬢ Such methods offer greater diagnostic efficiency and the ability to identify individualized treatment recommendations for maximal treatment outcomes

77

Final ConsiderationsStrengths

Quick

Comprehensive

Highly granular

Individualized

Efficient

LimitationsLimited extant neuropsychological & biological research support for ASD functioning (much more for behavioral observations)

Unavoidable noise (differing working definitions across fields)

Methodology is grounded in the DSM-5, but it extends beyond strict DSM-5 parameters

78

ReferencesAmerican Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing

Baoi, J., Wiggins, L., Christensen, D., … & Dowling, M. (2018). Prevalence of autism spectrum disorder among children aged 8 years: Autism and developmental disabilities monitoring network. Surveillance Summary, 67(6), 1-23. Retrieved from https://www.cdc.gov

Broderick, G., & Craddock, T.J. (2013). Systems biology of complex symptom profiles: Capturing interactivity across behavior, brain and immune regulation. Brain, Behavior, and Immunity, 29, 1-8. doi: 10.1016/j.bbi.2012.09.008

Broderick, G., Katz, B. Z., Fernandez, H., et al. (2012). Cytokine expression profiles of immune imbalance in post-mononucleosis chronic fatigue. Journal of Translational Medicine, 10(1). 191.

Brooks, B., Sherman, E., & Strauss, E. (2010). NEPSY-II: A Developmental Neuropsychological Assessment, 2nd Edition. Child Neuropsychology, 16, 80-81. doi: 10.1080/09297040903146966

Carlson, J., Geisinger, K., & Johnson, J. (2017). The Gilliam Autism Rating Scale, Third Edition (GARS-3). The Twentieth Mental Measurements Yearbook

Choi, B., & Pak, A. (2006). Multidisciplinary, interdisciplinary and transdisciplinary in health research, services, education and policy: Definitions, objectives, and evidence of effectiveness. Clinical and Investigative Medicine, 29(6), 351-364.

Craddock, T., Del Rosario, R., Rice, M., … & Broderick, G. (2015). Achieving remission in Gulf War illness: A simulation-based approach to treatment design. PLOSone. doi:10.1371/journal.pone.0132774

Craddock, T.J., Fritsch, P., Rice, M., Del Rosario, R., … & Broderick, G. (2014). A role for homeostatic drive in the perpetuation of chronic illness: Gulf war illness and chronic fatigue syndrome. PLOS One, 9(1). doi: 10.1371/journal.pone.0084839

Dawson, M., Soulieres, I, … & Mottron, L. (2007). The level and nature of autistic intelligence. Psychological Science, 18(8), 657-662. doi: 10.1111/j.1467-9280.2007.01954.x

Floris, D., & Howells, H. (2018). Atypical structural and functional motor networks in autism. Progress in Brain Research, 238, 207-248. doi: 10.1016/bs.pbr.2018.06.010

Folcik, V., Broderick, G., Mohan, S., …& Marsh, C. B. (2011). Using an agent-based model to analyze the mechanistic communication network of the immune response. Theoretical Biology and Medical Modelling, 8(1). http://www.tbiomed.com/content/8/1/1

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References (cont.)Gardner, J., Toole, J. T., Kalia, H., Spink, G., & Broderick, G. (2021). Knowing what we know: Leveraging community knowledge through automated text-mining. Advances in Clinical Medical Research and Healthcare Delivery, 1(1). Retrieved from https://scholar.rochesterregional.org/advances/vol1/iss1/2

Harvey, P. (2012). Clinical applications of neuropsychological assessment. Dialogues in Clinical Neuroscience, 14(1), 91-99. doi: 10.31887/DCNS.2012.14.1/pharvey

Koziol, L.F., Beljan, P., Bree, K., et al. (2016). Large-Scale Brain Systems and Neuropsychological Testing: An Effort to Move Forward. Switzerland: Springer International Publishing

Koziol, L.F., Barker, L.A., Joyce, A.W., & Hrin, S. (2014). Large-scale brain systems and subcortical relationships: The vertically organized brain. Applied Neuropsychology Child, 3(4), 253-263. doi: 10.1080/2122965.2014.946804

McCrimmon, A. & Rostad, K. (2014). Test Review, Autism Diagnostic Observation Schedule, Second Edition (ADOS-2). Psychoeducational Assessment, 32(1), 88-92. Retrieved from https://journal-sagepub-com

Parsons, T., & Duffield, T. (2020). Paradigm shift toward digital neuropsychology and high-dimensional neuropsychological assessments: Review. Journal of Medical Internet Research, 22(12). Doi: 10.2196/23777

Toole, J.T., et al. (2018). Increasing resilience to traumatic stress: Understanding the protective role of well-being. In: Yan Q. (Eds). Psychoneuroimmunology: Methods in Molecular Biology, 1781. New York, NY: Humana Press

Webb, J., Beljan, P., et al. (2004). Misdiagnosis and Dual Diagnosis of Gifted Children and Adults: ADHD, Bipolar, OCD, Asperger’s, Depression, and Other Disorders, 2nd Ed. Tucson, AZ: Great Potential Press

Wechsler, D. (2014). WISC-V Administration and Scoring Manual. Bloomington, MN. NCS Pearson, Inc.

Weiss, L., Munoz, M., & Prifitera, A. (2016). Testing Hispanics with the WISC-V and WISC-IV. WISC-V Assessment and Interpretation Scientist-Practitioner Perspectives, 215-236

Woodbury-Smith, & Sherer, S. (2018). Progress in the genetics of autism spectrum disorder. Developmental Medicine & Child Neurology, 60, 445-451. doi: 10.1111/dmcn.13717

Zander, E., & Bolte, S. (2015). The added value of the combined use of the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule: Diagnostic validity in a clinical Swedish sample of toddlers and young preschoolers. Autism, 19(2), 187-199. doi: 10.1177/1362361313516199

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Panel Discussion

81

Paul BeljanPsyD, ABPdN, ABN

Pediatric Neuropsychologist

Dustin HowardPsyD

Licensed Clinical Psychologist

Justin GardnerPsyD

Postdoctoral Fellow

1. Identify two subtle nuances in interpreting WISC V data2. Describe at least one way the WISC V is an appropriate measure

for Spanish-speaking bilingual children whose secondary language is English

3. Name a neuropsychologically based behavioral management method

4. List two principles components of a psychological evaluation that research suggests are most predictive in diagnosing Autism Spectrum Disorder (ASD)

5. Identify integrative and individualized treatment interventions in Autism Spectrum Disorder for maximal therapeutic outcomes

6. Identify two key benefits and limitations to utilizing novel computational approaches to the diagnostic and treatment process of complex pervasive psychological disorders

Feel Free to Contact Us!Paul Beljan, PsyD, ABPdN, ABNBeljan Psychological Services9835 E. Bell Rd., Ste. 140Scottsdale, AZ 85260(602) [email protected]

Dustin Howard, PsyDDesierto Psychological, PLLC(480) [email protected]

Justin Gardner, PsyDBeljan Psychological Services(602) [email protected]

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Documents

Current and Future Assessment, Identification, and