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Pervasive Development Disorders

Pervasive Developmeent Disorders are a group of neurodevelopmental conditions characterized by persistent difficulties in social communication and interaction, as well as restricted and repetitive patterns of behavior, interests, or activities.
These disorders typically manifest early in childhood and can significantly impact an individual's functioning across various settings.
The spectrum includes Autism Spectrum Disorder, Rett Syndrome, and Childhood Disintegrative Disorder, among others.
Ongoing research aims to better understand the underlying causes, improve diagnostic techniques, and develop effective interventions to support individuals with Pervasive Development Disorders and their families.

Most cited protocols related to «Pervasive Development Disorders»

Benchmarking Protein Disorder Predictors

Cited 85 times

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Publication 2010

Cells Crystallography, X-Ray Disulfides Ligands Pervasive Development Disorders Proteins Radiography Tissue, Membrane

Genome-wide Overlap Across Psychiatric Disorders

Cited 78 times

Genome-wide significant loci for BD were assessed for overlap with genome-wide significant loci for other psychiatric disorders, using the largest available GWAS results for major depression^{61 (link)}, schizophrenia^{60 (link)}, attention deficit/hyperactivity disorder¹⁰¹, post-traumatic stress disorder¹⁰², lifetime anxiety disorder¹⁰³, Tourette’s Syndrome¹⁰⁴, anorexia nervosa¹⁰⁵, alcohol use disorder or problematic alcohol use^{68 (link)}, autism spectrum disorder¹⁰⁶, mood disorders^{91 (link)} and the cross-disorder GWAS of the Psychiatric Genomics Consortium^{66 (link)}. The boundaries of the genome-wide significant loci were calculated in the original publications. Overlap of loci was calculated using bedtools v2.29.2¹⁰⁷.

Mullins N., Forstner A.J., O’Connell K.S., Coombes B., Coleman J.R., Qiao Z., Als T.D., Bigdeli T.B., Børte S., Bryois J., Charney A.W., Drange O.K., Gandal M.J., Hagenaars S.P., Ikeda M., Kamitaki N., Kim M., Krebs K., Panagiotaropoulou G., Schilder B.M., Sloofman L.G., Steinberg S., Trubetskoy V., Winsvold B.S., Won H.H., Abramova L., Adorjan K., Agerbo E., Al Eissa M., Albani D., Alliey-Rodriguez N., Anjorin A., Antilla V., Antoniou A., Awasthi S., Baek J.H., Bækvad-Hansen M., Bass N., Bauer M., Beins E.C., Bergen S.E., Birner A., Pedersen C.B., Bøen E., Boks M.P., Bosch R., Brum M., Brumpton B.M., Brunkhorst-Kanaan N., Budde M., Bybjerg-Grauholm J., Byerley W., Cairns M., Casas M., Cervantes P., Clarke T.K., Cruceanu C., Cuellar-Barboza A., Cunningham J., Curtis D., Czerski P.M., Dale A.M., Dalkner N., David F.S., Degenhardt F., Djurovic S., Dobbyn A.L., Douzenis A., Elvsåshagen T., Escott-Price V., Ferrier I.N., Fiorentino A., Foroud T.M., Forty L., Frank J., Frei O., Freimer N.B., Frisén L., Gade K., Garnham J., Gelernter J., Pedersen M.G., Gizer I.R., Gordon S.D., Gordon-Smith K., Greenwood T.A., Grove J., Guzman-Parra J., Ha K., Haraldsson M., Hautzinger M., Heilbronner U., Hellgren D., Herms S., Hoffmann P., Holmans P.A., Huckins L., Jamain S., Johnson J.S., Kalman J.L., Kamatani Y., Kennedy J.L., Kittel-Schneider S., Knowles J.A., Kogevinas M., Koromina M., Kranz T.M., Kranzler H.R., Kubo M., Kupka R., Kushner S.A., Lavebratt C., Lawrence J., Leber M., Lee H.J., Lee P.H., Levy S.E., Lewis C., Liao C., Lucae S., Lundberg M., MacIntyre D.J., Magnusson S.H., Maier W., Maihofer A., Malaspina D., Maratou E., Martinsson L., Mattheisen M., McCarroll S.A., McGregor N.W., McGuffin P., McKay J.D., Medeiros H., Medland S.E., Millischer V., Montgomery G.W., Moran J.L., Morris D.W., Mühleisen T.W., O’Brien N., O’Donovan C., Olde Loohuis L.M., Oruc L., Papiol S., Pardiñas A.F., Perry A., Pfennig A., Porichi E., Potash J.B., Quested D., Raj T., Rapaport M.H., DePaulo J.R., Regeer E.J., Rice J.P., Rivas F., Rivera M., Roth J., Roussos P., Ruderfer D.M., Sánchez-Mora C., Schulte E.C., Senner F., Sharp S., Shilling P.D., Sigurdsson E., Sirignano L., Slaney C., Smeland O.B., Smith D.J., Sobell J.L., Hansen C.S., Artigas M.S., Spijker A.T., Stein D.J., Strauss J.S., Świątkowska B., Terao C., Thorgeirsson T.E., Toma C., Tooney P., Tsermpini E.E., Vawter M.P., Vedder H., Walters J.T., Witt S.H., Xi S., Xu W., Yang J.M., Young A.H., Young H., Zandi P.P., Zhou H., Zillich L., Adolfsson R., Agartz I., Alda M., Alfredsson L., Babadjanova G., Backlund L., Baune B.T., Bellivier F., Bengesser S., Berrettini W.H., Blackwood D.H., Boehnke M., Børglum A.D., Breen G., Carr V.J., Catts S., Corvin A., Craddock N., Dannlowski U., Dikeos D., Esko T., Etain B., Ferentinos P., Frye M., Fullerton J.M., Gawlik M., Gershon E.S., Goes F.S., Green M.J., Grigoroiu-Serbanescu M., Hauser J., Henskens F., Hillert J., Hong K.S., Hougaard D.M., Hultman C.M., Hveem K., Iwata N., Jablensky A.V., Jones I., Jones L.A., Kahn R.S., Kelsoe J.R., Kirov G., Landén M., Leboyer M., Lewis C.M., Li Q.S., Lissowska J., Lochner C., Loughland C., Martin N.G., Mathews C.A., Mayoral F., McElroy S.L., McIntosh A.M., McMahon F.J., Melle I., Michie P., Milani L., Mitchell P.B., Morken G., Mors O., Mortensen P.B., Mowry B., Müller-Myhsok B., Myers R.M., Neale B.M., Nievergelt C.M., Nordentoft M., Nöthen M.M., O’Donovan M.C., Oedegaard K.J., Olsson T., Owen M.J., Paciga S.A., Pantelis C., Pato C., Pato M.T., Patrinos G.P., Perlis R.H., Posthuma D., Ramos-Quiroga J.A., Reif A., Reininghaus E.Z., Ribasés M., Rietschel M., Ripke S., Rouleau G.A., Saito T., Schall U., Schalling M., Schofield P.R., Schulze T.G., Scott L.J., Scott R.J., Serretti A., Weickert C.S., Smoller J.W., Stefansson H., Stefansson K., Stordal E., Streit F., Sullivan P.F., Turecki G., Vaaler A.E., Vieta E., Vincent J.B., Waldman I.D., Weickert T.W., Werge T., Wray N.R., Zwart J.A., Biernacka J.M., Nurnberger J.I., Cichon S., Edenberg H.J., Stahl E.A., McQuillin A., Di Florio A., Ophoff R.A, & Andreassen O.A. (2021). Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature genetics, 53(6), 817-829.

Publication 2021

Alcohol Use Disorder Anorexia Anxiety Disorders Attention Deficit Disorder Ethanol Genome Genome-Wide Association Study Mental Disorders Mood Pervasive Development Disorders

Polygenic Risk Scores for Autism Spectrum Disorder

Cited 46 times

For the polygenic risk scores (PRS) we clumped the summary stats applying standard Ricopili parameters (see Supplementary Note for details). To avoid potential strand conflicts, we excluded all ambiguous markers for summary statistics not generated by Ricopili using the same imputation reference. PRS were generated at the default p-value thresholds (5e-8, 1e-6, 1e-4, 0.001, 0.01, 0.05, 0.1, 0.2, 0.5 and 1) as a weighted sum of the allele dosages in the ASD GWAS sample. Summing over the markers abiding by the p-value threshold in the training set and weighing by the additive scale effect measure of the marker (log(OR) or β) as estimated in the training set. Scores were normalized prior to analysis.
We evaluated the predictive power using Nagelkerke’s R² and plots of odds ratios and confidence intervals over score deciles. Both R² and odds ratios were estimated in regression analyses including the relevant PCs and indicator variables for genotyping waves.
Lacking a large ASD sample outside of iPSYCH and PGC, we trained a set of PRS for ASD internally in the following way. We divided the sample in five subsamples of roughly equal size respecting the division into batches. We then ran five GWAS leaving out each group in turn from the training set and meta-analyzed these with the PGC results. This produced a set of PRS for each of the five subsamples trained on their complement. Prior to analyses, each score was normalized on the group where it was defined. We evaluated the predictive power in each group and on the whole sample combined.
To exploit the genetic overlap with other phenotypes to improve prediction, we created a series of new PRS by adding to the internally trained ASD score the PRS of other highly correlated phenotypes in a weighted sum. See supplementary info for details.
To analyze ASD subtypes in relation to PRS we defined a hierarchical set of phenotypes in the following way: First hierarchical subtypes was childhood autism, hierarchical atypical autism was defined as everybody with atypical autism and no childhood autism diagnosis, hierarchical Asperger’s as everybody with an Asperger’s diagnosis and neither childhood autism nor atypical autism. Finally, we lumped other pervasive developmental disorders and pervasive developmental disorder, unspecified into pervasive disorders developmental mixed, and the hierarchical version of that consists of everybody with such a diagnosis and none of the preceding ones (Supplementary Table 13). We examined the distribution over the distinct ASD subtypes of PRS for a number of phenotypes showing high r_G with ASD (as well as a few with low r_G as negative controls), by doing multivariate regression of the scores on the subtypes while adjusting for relevant PCs and wave indicator variables in a linear regression. See Supplementary Note for details.

Grove J., Ripke S., Als T.D., Mattheisen M., Walters R.K., Won H., Pallesen J., Agerbo E., Andreassen O.A., Anney R., Awashti S., Belliveau R., Bettella F., Buxbaum J.D., Bybjerg-Grauholm J., Bækvad-Hansen M., Cerrato F., Chambert K., Christensen J.H., Churchhouse C., Dellenvall K., Demontis D., De Rubeis S., Devlin B., Djurovic S., Dumont A.L., Goldstein J.I., Hansen C.S., Hauberg M.E., Hollegaard M.V., Hope S., Howrigan D.P., Huang H., Hultman C.M., Klei L., Maller J., Martin J., Martin A.R., Moran J.L., Nyegaard M., Nærland T., Palmer D.S., Palotie A., Pedersen C.B., Pedersen M.G., Poterba T., Poulsen J.B., St Pourcain B., Qvist P., Rehnström K., Reichenberg A., Reichert J., Robinson E.B., Roeder K., Roussos P., Saemundsen E., Sandin S., Satterstrom F.K., Smith G.D., Stefansson H., Steinberg S., Stevens C.R., Sullivan P.F., Turley P., Walters G.B., Xu X., Stefansson K., Geschwind D.H., Nordentoft M., Hougaard D.M., Werge T., Mors O., Mortensen P.B., Neale B.M., Daly M.J, & Børglum A.D. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature genetics, 51(3), 431-444.

Publication 2019

Alleles Autistic Disorder Diagnosis Genome-Wide Association Study Pervasive Development Disorders Phenotype

Preschool Depression Study Recruitment

Cited 42 times

This investigation used data from a National Institute of Mental Health–funded Validation of Preschool Depression Study. The Preschool Depression Study is an ongoing, multi-method, multi-informant (parents, children, and teachers), longitudinal investigation of 306 preschoolers. Comprehensive assessments were conducted at 3 annual waves in the Early Emotional Development Program at the Washington University School of Medicine in St Louis, St Louis, Missouri. From May 2003 to March 2005, children aged 3 to 5.11 years were recruited from pediatricians’ offices, daycare centers, and preschools in the St Louis metropolitan area using the Preschool Feelings Checklist (PFC).34 Approximately 6000 PFCs were distributed to recruitment sites and 1474 PFCs (25%) were returned to the Early Emotional Development Program. Caregivers who endorsed no items on the PFC; 2 or more internalizing items; and/or 2 or more externalizing items (n = 899) were contacted by telephone for further screening. Excluded were children with chronic medical illnesses, neurological problems, pervasive developmental disorders, or language and/or cognitive delays as well as those outside of the study age range. Of the 416 eligible caregiver-child dyads, 306 agreed to participate and presented for baseline assessment; details have been published previously.24 (link) It is important to note that the recruitment techniques used in this study were designed to oversample for preschoolers with or at risk of depression. Therefore, the recruitment numbers provided cannot be used to estimate the prevalence rates of preschool MDD in the general population.

Luby J.L., Si X., Belden A.C., Tandon M, & Spitznagel E. (2009). Preschool Depression: Homotypic Continuity and Course Over 24 Months. Archives of general psychiatry, 66(8), 897-905.

Publication 2009

Child Child, Preschool Cognition Disease, Chronic Emotions Feelings Parent Pediatricians Pervasive Development Disorders Pharmaceutical Preparations Program Development

Validating PDDS: Correlating Disability Measures

Cited 26 times

To establish criterion validity, the correlation between PDDS and EDSS scores were examined as the EDSS is the most common and accepted measure of disability status in MS. To establish the convergent and divergent aspects of construct validity, we examined the correlations between PDDS scores with FS scores and other clinical outcomes. The correlations with measures related to mobility (i.e., pyramidal functions, cerebellar functions, sensory functions, 6 MW, T25FW, TUG, steps/day, BLEF and ALEF) provided information on the convergent validity of the PDDS, whilst comparisons with outcomes related to other, non-mobility constructs (i.e. optic functions, brainstem functions, bowel/bladder functions, mental status function, demographic variables, UEF, SDMT and PASAT) provide information on the divergent validity of the PDDS.

Learmonth Y.C., Motl R.W., Sandroff B.M., Pula J.H, & Cadavid D. (2013). Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurology, 13, 37.

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Publication 2013

Brain Stem Cerebellum Defecation Disabled Persons Eye Pervasive Development Disorders Range of Motion, Articular Respiratory Diaphragm Urinary Bladder

Most recents protocols related to «Pervasive Development Disorders»

Nrxn1α Deletion Mouse Models

Mice were group housed in cages of 2 to 5 on a 12-hour light/12-hour dark cycle with food and water provided ad libitum. The mice used in this study for behavioral testing were between 3–5 months of age, including both males and females.
Mice with Nrxn1α promoter and exon 1 deletion (ΔExon1) were described previously [9 (link)] and have been maintained in C57BL/6J background. Mice with Nrxn1α exon 9 deletion (ΔExon9) were generated by crossing an exon 9 floxed allele of Nrxn1α (Nrxn1^{tm1a(KOMP)Wtsi} from MRC Mary Lyon Center, Harwell, UK) [13 (link)] with mice carrying UBC-CreERT2 [70 (link)]. An unexpected leaky activity of Cre in male gametes [33 (link)] carrying both floxed exon 9 of Nrxn1α and UBC-Cre-ERT2 leads to a germline loss of exon 9 (ΔExon9). The deletion of exon 9 was confirmed by PCR analysis using primers flanking the deleted region and within the exon 9 sequence. To study a CNV identified in an individual on the autism spectrum [27 (link)], mouse homologue of the ~20 kb deleted region at intron 17 of Nrxn1α was identified and deleted using the CRISPR/Cas9-medicated genomic editing approach. Two sgRNAs (5’ AATATGTGGGCAAGCTGGGT TGG 3’ and 5’ GAAATGGTACCTTTGATCTA AGG 3’) flanking the deletion region in intron 17 of Nrxn1α were injected together with Cas9 protein into 1-cell zygote of C57BL/6J/SJL genetic background. The target deletion was confirmed by PCR and sequencing analyses using primers flanking the deleted region and deletion carriers (ΔIntron17) were back crossed to C57BL/6J for 5 more generations to collect littermates for behavioral phenotyping.
To generate experimental animals used in this study, heterozygous males were bred with heterozygous females to generate mice with homozygous (ΔExon9/ΔExon9; ΔIntron17/ΔIntron17) or heterozygous (ΔExon9/+; ΔIntron17/+) deletions, as well as WT littermates (+/+). We noted that mice carrying homozygous deletion of exon 9 (ΔExon9/ΔExon9) were significantly underrepresented with WT:Het:Homo ratio as 48:90:29, in contrast to the expected ratio of 42:83:42, indicating sub-viability in mice carrying a complete loss of Nrxn1α. To generate mice carrying ΔExon1, heterozygous carriers of ΔExon1 were bred with WT mice to collect heterozygotes and WT for experiments described in this study.

Xu B., Ho Y., Fasolino M., Medina J., O’Brien W.T., Lamonica J.M., Nugent E., Brodkin E.S., Fuccillo M.V., Bucan M, & Zhou Z. (2023). Allelic contribution of Nrxn1α to autism-relevant behavioral phenotypes in mice. PLOS Genetics, 19(2), e1010659.

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Publication 2023

Alleles Animals, Laboratory Cells Clustered Regularly Interspaced Short Palindromic Repeats CRISPR-Associated Protein 9 Deletion Mutation Exons Females Food Gametes Gene Deletion Genetic Background Germ Line Heterozygote Homo Homozygote Introns Males Mice, House mitogen-activated protein kinase 3, human Oligonucleotide Primers Pervasive Development Disorders Sequence Analysis Zygote

Autism Diagnosis Protocols in Scania

The outcome of interest in this study was autism, specifically ASD [International Classification of Mental and Behavioral Disorders version 10 (ICD-10) diagnosis codes starting with F84] and childhood autism (ICD-10 diagnosis code F84.0) only. ASD comprises all pervasive developmental disorders. It is characterized by one or more of the following areas of neurodivergence: qualitative variations in patterns of communication; difficulties with reciprocal social interactions; and a restricted, repetitive collection of behaviors and interests. For childhood autism, symptoms within all three areas of neurodivergence must be present before the age of 3 years.
When a child is suspected of having autism in Scania, they are referred to the Departments of Child and Adolescent Psychiatry and are examined by a multidisciplinary team^{42 (link)}. These evaluations utilize both the Autism Diagnostic Observation Schedule-Generic (ADOS-G)^{43 (link)} and the Autism Diagnostic Interview-Revised (ADI-R)^{44 (link)} for most (75%) cases. Diagnostic methods for the remaining cases can differ. On occasion, structured instruments other than ADOS and ADI, such as the Mini-International Neuropsychiatric Interview for Children and Adolescents (MINI Kid), the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS), the Social Communication Questionnaire (SCQ), the Social Responsiveness Scale, 2nd Edition (SRS-2), the Child Behavior Checklist (CBCL), or the Nordic Questionnaire for Evaluation of Development and Behavior in Children and Adolescents called Five-to-Fifteen (5–15 or FTF), are used to varying degrees and in varying combinations. In all cases, however, the child’s behavior is observed, the parents are interviewed, and information is gathered from the child’s school. All data is then evaluated and compared to diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Finally, an autism diagnosis is assigned according to the ICD-10 and entered into the Skåne Healthcare Database (SHR). The outcome data used in this study was extracted from SHR and was available through the 31st of December, 2017.

Flanagan E., Malmqvist E., Rittner R., Gustafsson P., Källén K, & Oudin A. (2023). Exposure to local, source-specific ambient air pollution during pregnancy and autism in children: a cohort study from southern Sweden. Scientific Reports, 13, 3848.

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Publication 2023

Adenosine Adolescent Autistic Disorder Behavior Disorders Child Diagnosis Generic Drugs Parent Pervasive Development Disorders Schizophrenia

Gait Analysis in Autistic Children

We utilized the freeware tool GPower (http://www.gpower.hhu.de/) to calculate a one-sided a priori power analysis. The a priori power analysis was calculated using the F-test family (i.e., ANOVA repeated measures within-between interaction), and a related study that examined the effects of training on horizontal ground reaction force in ASD Children [22 (link)]. The included program variables were an assumed type I error of 0.05, a type II error rate of 0.20 (80% statistical power), and an effect size of 0.70 for horizontal ground reaction force taken from the reference study [22 (link)]. The analysis revealed that at least 12 participants would be needed per group to achieve medium- to large-sized interaction effects for the parameter horizontal ground reaction force. Accordingly, a total of twenty-four prepubertal ASD boys aged 7–11 years were recruited from a group of children who participated in an adapted physical activity program that was delivered in a local community center. The scores of the “Gilliam Autism Rating Scale-2” [23 ] for the participating ASD children were between 62 and 123 which shows that the participating children were diagnosed as ASD children. The enrolled boys were age-matched and randomly allocated to an experimental (n = 12) or a waiting control group (n = 12) (Fig. 1). Participants’ anthropometric and demographic data are presented in Table 1. The following information was gathered from all participating children: date of birth, intensity, and date of autism anamnesis. Children with Asperger’s or pervasive developmental disorder were excluded from this study. A medical doctor examined all participants prior to the start of the study and excluded those with neuromotor or orthopedic disorders or medication that could affect the central nervous system. None of the participants reported any secondary neurological or orthopedic conditions including lower limb injuries during the 12 months prior to data collection. ASD disorder status and the presence or absence of learning disabilities were assessed using an Iranian translation of the Social Communication Questionnaire [24 , 25 ] and the Persian translation of the Autism Diagnostic Interview [26 , 27 ] by the medical doctor. We obtained children’s oral consent and parents’ or legal representatives’ written consent before the start of the study. The block randomization method (block size = 4) was used to allocate study participants into the experimental groups [28 (link)]. A naïve examiner realized the block randomization process. During the randomization procedure, a set of sealed, opaque envelopes was used to ensure the concealment of the allocation. Each envelope contained a card stipulating to which group the participant would be allocated to. Of note, participants were blinded to the group allocation. One examiner determined whether a participant was eligible for inclusion, while the other carried out gait analyses of the eligible participants. Both examiners were unaware of the group allocation. Another naïve examiner (i.e., physiotherapist with 10 years of professional experience) controlled the allocation of each participant and was responsible for delivering the treatment to both groups. This study was approved by the Ethical Committee of the University of Mohaghegh Ardabili, Ardabil, Iran (IR.UMA.REC.1400.019) and registered at the Iranian Registry of Clinical Trials (IRCT20170806035517N4). The study was conducted in accordance with the latest version of the Declaration of Helsinki. The data were collected at sport biomechanics laboratory of University of Mohaghegh Ardabili, Ardabil, Iran.Fig. 1

CONSORT flow diagram of the present study

Table 1

Group-specific baseline characteristics of the study participants

	Intervention (n = 12)	Waiting control (n = 12)	Significance level
Age (years)	9.2 ± 0.6	9.4 ± 0.5	0.904
Body mass (kg)	36.70 ± 2.47	36.70 ± 3.38	1.000
Body height (cm)	128.45 ± 4.84	130.00 ± 4.81	0.443
BMI (kg/m²)	22.29 ± 1.85	21.80 ± 2.61	0.599

Values are means ± standard deviations

n number of participants, BMI body mass index, NA not applicable

Dehghani M., Jafarnezhadgero A.A., Darvishani M.A., Aali S, & Granacher U. (2023). Effects of an 8-week multimodal exercise program on ground reaction forces and plantar pressure during walking in boys with autism spectrum disorder. Trials, 24, 170.

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Publication 2023

Autistic Disorder Biomechanical Phenomena Boys Central Nervous System Child Childbirth Diagnosis Gait Analysis Immunologic Memory Index, Body Mass Learning Disabilities Leg Injuries Musculoskeletal Diseases neuro-oncological ventral antigen 2, human Parent Pervasive Development Disorders Pharmaceutical Preparations Physical Therapist Physicians

Autism Diagnostic Observation Schedule

The Autism Diagnostic Observation Schedule 2nd Edition [32 ] is a standardized semi-structured interview recommended for the assessment of ASD, generally lasting from 30 to 60 min. It includes a range of questions and activities designed to evoke behaviors and cognitions associated with ASD. These visible behaviors and discussions are then scored from 0 to 3 for “autism severity”. Under the original algorithm, 11 items from the larger scoring matrix are then summed to create an ADOS score, where 7 is the cut-off for being designated as “on the autism spectrum,” and 10 is the cut-off for being designated as “autistic.” The algorithm has two subscales: social affect and restrictive and repetitive behaviors, and total scores of 8 or more indicate possible ASD.

Chang X., Sun L, & Li R. (2023). Application of symbolic play test in identification of autism spectrum disorder without global developmental delay and developmental language disorder. BMC Psychiatry, 23, 138.

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Publication 2023

Adenosine Autistic Disorder Cognition Diagnosis Pervasive Development Disorders

Decision-Making in Autism: An Empirical Protocol

All tasks were delivered online. Participants first signed a written consent form and completed demographic information (e.g., age, education, gender, prior diagnoses). Those in the autism group also uploaded their diagnosis certificate (see details in S1 File). All participants then performed the IGT. In this task participants are presented with four decks of cards labeled A, B, C, and D. They can select a single card from these four decks in each trial. After selecting each card, participants receive token money (the amount is displayed on the screen). Task payoffs are presented in Table 1 and screen illustrations appear in S1 File. Two of the decks are advantageous and produce lower gains but somewhat lower (uncertain) losses; these have positive expected values. The other two decks are disadvantageous and produce higher gains but also higher (uncertain) losses; these have negative expected values. The cumulative payoff is presented at the bottom of the display and is updated at the end of each trial. The display also includes the amount given to participants at the beginning of the task as a “loan”. The initial loan in our study was $3500. The minimum inter-trial interval was set to one second, and the task included 120 trials, which were analyzed by dividing them into four 30-trials blocks. Participants were given verbal instructions identical to those provided in Johnson et al. [4 (link)] (see S1 File). Following the standard version of the IGT there was another block of trials with no payoff feedback. This trial block was administered at the end so that participants first learn the incentive structure of the task. Given the fact that there was no feedback we felt that 30 trials would be sufficient to gage the participants’ responses. Prior to this no-feedback block, participants were instructed that over the next trials they would not receive any payoff feedback. Amounts were converted to actual money at the end of the task at a rate of $1 for each $1500 of token money.
Next, participants completed verbal and non-verbal brief intellectual aptitude tests. The verbal test was the Multidimensional Aptitude Battery (MAB; [26 ]), a modified Similarities subscale from the Wechsler Abbreviated Scale of Intelligence (WASI; [27 ]). The non-verbal test was the Raven Standard Progressive Matrices (RSPM, Set 1; [28 ]).
Finally, in order to validate group differences, we administered additional self-report questionnaires for autism-related symptoms, the Autism Spectrum Quotient (AQ10) [29 (link)] and the Social Responsiveness Scale, 2nd Edition (SRS-2) (adult self-report version [30 ]). The AQ10 is a self-administered ten-item questionnaire used for measuring where adults lie on the autism spectrum or continuum. Though there are some findings questioning the reliability and validity of the AQ10 [31 (link), 32 ], we used it as a brief validation of the documented diagnosis. The SRS-2 is a 65-item questionnaire that assesses difficulties in reciprocal social behavior that lead to interference with everyday social interactions.

Zeif D., Yakobi O, & Yechiam E. (2023). Choice behavior in autistic adults: What drives the extreme switching phenomenon?. PLOS ONE, 18(3), e0282296.

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Publication 2023

Adult Aptitude Aptitude Tests Autistic Disorder Cardiac Arrest Diagnosis Feelings Gender Pervasive Development Disorders Wechsler Scales

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Spss statistics 24 by IBM

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SPSS Statistics 24 is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and presentation. The software is designed to handle a wide range of data types and offers a user-friendly interface for conducting various statistical procedures.

Oragene og 500 collection kit by DNA Genotek

Sourced in Canada

The Oragene OG-500 collection kit is a self-collection device designed to collect and stabilize DNA samples from saliva. The kit provides a simple and convenient method for individuals to collect their own DNA sample without the need for a healthcare professional.

Nimblegen seqcap ez exome v2 capture library by Roche

Sourced in United States

The NimbleGen SeqCap EZ Exome v2 capture library is a tool designed for targeted sequencing of the human exome. It provides a comprehensive solution for capturing and enriching the protein-coding regions of the genome, which represent the most well-characterized and clinically relevant portion of the human genome.

What are the different types of Pervasive Development Disorders?

Pervasive Development Disorders (PDDs) encompass a spectrum of neurodevelopmental conditions that share common characteristics, such as persistent difficulties in social communication and interaction, as well as restricted and repetitive patterns of behavior. The main types of PDDs include: 1. Autism Spectrum Disorder (ASD): The most prevalent type, characterized by challenges in social interaction, communication, and the presence of restricted, repetitive behaviors. 2. Rett Syndrome: A rare genetic disorder that primarily affects girls, leading to developmental delays, loss of motor skills, and other neurological symptoms. 3. Childhood Disintegrative Disorder (CDD): A rare condition where a child experiences a significant loss of previously acquired skills, often in the areas of language, social, and motor function. 4. Pervasive Development Disorder - Not Otherwise Specified (PDD-NOS): A diagnosis used for individuals who exhibit some, but not all, of the characteristics of ASD or other PDDs.

How can PubCompare.ai assist in Pervasive Development Disorders research?

PubCompare.ai is an AI-driven platform that can significantly enhance your Pervasive Development Disorders (PDDs) research in several ways: 1. Efficient protocol screening: The platform allows you to quickly and effectively sift through the vast amount of literature on PDDs, helping you identify the most relevant protocols for your specific research goals. 2. AI-powered insights: PubCompare.ai's advanced AI algorithms can provide critical insights by highlighting key differences in the effectiveness and performance of various protocols related to PDDs. This enables you to make informed decisions on the best protocols to use for your research. 3. Optimizing research protocols: By leveraging the platform's AI-powered comparisons, you can fine-tune and optimize your research protocols for PDDs, ensuring reproducibility, accuracy, and improved outcomes. 4. Identifying the most effective products: PubCompare.ai can help you locate the most effective products, tools, and technologies to support your PDDs research, allowing you to make informed purchasing decisions.

What are some common challenges in working with Pervasive Development Disorders?

Researchers and clinicians working with Pervasive Development Disorders (PDDs) often face several challenges, including: 1. Heterogeneity: PDDs encompass a broad spectrum of conditions, each with its own unique characteristics and presentations. This heterogeneity can make it difficult to develop universal treatment approaches and interventions. 2. Early identification and diagnosis: Accurately diagnosing PDDs, especially in young children, can be challenging due to the complex and overlapping symptoms. Early intervention is crucial, but it requires timely and accurate diagnosis. 3. Comorbidities: Individuals with PDDs often experience comorbid conditions, such as intellectual disabilities, anxiety, and sensory processing issues. Managing these complicating factors can be complex. 4. Lack of standardized protocols: Due to the diversity of PDDs, there is a need for more standardized research protocols and interventions that can be consistently applied across different settings and populations.

How can PubCompare.ai help researchers optimize their Pervasive Development Disorders protocols?

PubCompare.ai's AI-driven platform can be invaluable in optimizing research protocols for Pervasive Development Disorders (PDDs) in several ways: 1. Identifying the most effective protocols: The platform's advanced algorithms can analyze and compare a wide range of protocols from published literature, preprints, and patents. This helps researchers identify the most effective protocols for their specific PDDs research objectives. 2. Highlighting key differences: PubCompare.ai's AI-powered analysis can pinpoint critical differences in the performance and outcomes of various PDDs protocols. This enables researchers to make informed decisions on the best protocols to use for their studies. 3. Enhancing reproducibility and accuracy: By selecting the most effective and reliable protocols, researchers can improve the reproducibility and accuracy of their PDDs studies, leading to more robust and meaningful findings. 4. Streamlining the research process: The platform's efficient protocol screening and comparison capabilities can save researchers valuable time and resources, allowing them to focus on advancing their PDDs research more effectively.

What are some unique applications of PubCompare.ai for Pervasive Development Disorders research?

PubCompare.ai offers several unique applications that can benefit researchers working on Pervasive Development Disorders (PDDs): 1. Identifying novel interventions: The platform's comprehensive database and AI-powered analysis can help researchers discover innovative interventions, therapies, and approaches that may not be widely known or published yet. 2. Comparing protocol variations: PubCompare.ai can analyze different variations of PDDs protocols, such as those targeting specific symptoms or age groups, to help researchers identify the most effective approaches. 3. Evaluating emerging technologies: As new technologies, such as assistive devices or digital therapies, are developed for PDDs, PubCompare.ai can assist researchers in comparing their effectiveness and suitability for different applications. 4. Supporting multi-site collaborations: The platform's ability to streamline protocol comparisons and highlight key insights can facilitate collaboration among researchers working on PDDs across different institutions and settings.

More about "Pervasive Development Disorders"

Pervasive Development Disorders (PDDs) are a group of neurodevelopmental conditions characterized by persistent challenges in social communication and interaction, as well as restricted and repetitive patterns of behavior, interests, or activities.
This spectrum includes Autism Spectrum Disorder (ASD), Rett Syndrome, and Childhood Disintegrative Disorder, among others.
Early identification and intervention are crucial for individuals with PDDs, as these conditions can significantly impact an individual's functioning across various settings, such as home, school, and the community.
Ongoing research aims to better understand the underlying causes, improve diagnostic techniques, and develop effective interventions to support individuals with PDDs and their families.
The research process for PDDs often involves the use of various tools and technologies, such as 3T magnetic resonance imaging (MRI) scanners, MATLAB for data analysis, ELISA (Enzyme-Linked Immunosorbent Assay) methods for biomarker detection, and Whole-Genome 2.7M Array for genetic analysis.
Statistical software like Stata/SE 17.0, SPSS 28.0, SPSS Statistics 2016, and SPSS Statistics 24 are commonly used for data analysis and interpretation.
Sample collection methods, such as the Oragene OG-500 collection kit, and genetic sequencing techniques, like the NimbleGen SeqCap EZ Exome v2 capture library, may also be employed in PDD research.
By leveraging these advanced tools and techniques, researchers can gain deeper insights into the underlying mechanisms of PDDs, develop more accurate diagnostic methods, and design more effective interventions to support individuals with these complex neurodevelopmental conditions.