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Article ; Online: Prototyping as subtyping strategy for studying heterogeneity in autism.

Lombardo, Michael V

Autism research : official journal of the International Society for Autism Research

2021 Volume 14, Issue 10, Page(s) 2224–2227

MeSH term(s)	Autism Spectrum Disorder ; Autistic Disorder ; Humans
Language	English
Publishing date	2021-06-02
Publishing country	United States
Document type	Letter ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2481338-2
ISSN	1939-3806 ; 1939-3792
ISSN (online)	1939-3806
ISSN	1939-3792
DOI	10.1002/aur.2535
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Article ; Online: Ribosomal protein genes in post-mortem cortical tissue and iPSC-derived neural progenitor cells are commonly upregulated in expression in autism.

Lombardo, Michael V

Molecular psychiatry

2020 Volume 26, Issue 5, Page(s) 1432–1435

MeSH term(s)	Autism Spectrum Disorder/genetics ; Autistic Disorder ; Humans ; Induced Pluripotent Stem Cells ; Ribosomal Proteins/genetics ; Transcriptome
Chemical Substances	Ribosomal Proteins
Language	English
Publishing date	2020-05-13
Publishing country	England
Document type	Letter ; Comment
ZDB-ID	1330655-8
ISSN	1476-5578 ; 1359-4184
ISSN (online)	1476-5578
ISSN	1359-4184
DOI	10.1038/s41380-020-0773-x
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Zs.A 4812: Show issues

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Article: Rethinking Our Concepts and Assumptions About Autism.

Lombardo, Michael V / Mandelli, Veronica

Frontiers in psychiatry

2022 Volume 13, Page(s) 903489

Abstract: Autism is a clinical consensus diagnosis made based on behavioral symptoms of early developmental difficulties in domains of social-communication (SC) and restricted repetitive behaviors (RRB). Many readily assume that alongside being optimal for ... ...

Abstract	Autism is a clinical consensus diagnosis made based on behavioral symptoms of early developmental difficulties in domains of social-communication (SC) and restricted repetitive behaviors (RRB). Many readily assume that alongside being optimal for separating individuals based on SC and RRB behavioral domains, that the label should also be highly useful for explaining differential biology, outcomes, and treatment (BOT) responses. However, we also now take for granted the fact that the autism population is vastly heterogeneous at multiple scales, from genome to phenome. In the face of such multi-scale heterogeneity, here we argue that the concept of autism along with the assumptions that surround it require some rethinking. While we should retain the diagnosis for all the good it can do in real-world circumstances, we also call for the allowance of multiple other possible definitions that are better tailored to be highly useful for other translational end goals, such as explaining differential BOT responses.
Language	English
Publishing date	2022-06-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564218-2
ISSN	1664-0640
ISSN	1664-0640
DOI	10.3389/fpsyt.2022.903489
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Article ; Online: reval: A Python package to determine best clustering solutions with stability-based relative clustering validation.

Landi, Isotta / Mandelli, Veronica / Lombardo, Michael V

Patterns (New York, N.Y.)

2021 Volume 2, Issue 4, Page(s) 100228

Abstract: Determining the best partition for a dataset can be a challenging task because of the lack ... ...

Abstract	Determining the best partition for a dataset can be a challenging task because of the lack of
Language	English
Publishing date	2021-04-02
Publishing country	United States
Document type	Journal Article
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2021.100228
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Article ; Online: Big data approaches to decomposing heterogeneity across the autism spectrum.

Lombardo, Michael V / Lai, Meng-Chuan / Baron-Cohen, Simon

Molecular psychiatry

2019 Volume 24, Issue 10, Page(s) 1435–1450

Abstract: Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi- ... ...

Abstract	Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.
MeSH term(s)	Autism Spectrum Disorder/classification ; Autism Spectrum Disorder/genetics ; Autism Spectrum Disorder/physiopathology ; Big Data ; Case-Control Studies ; Genetic Heterogeneity ; Humans ; Longitudinal Studies
Language	English
Publishing date	2019-01-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1330655-8
ISSN	1476-5578 ; 1359-4184
ISSN (online)	1476-5578
ISSN	1359-4184
DOI	10.1038/s41380-018-0321-0
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Article ; Online: Autism and talent: the cognitive and neural basis of systemizing.

Baron-Cohen, Simon / Lombardo, Michael V

Dialogues in clinical neuroscience

2018 Volume 19, Issue 4, Page(s) 345–353

Abstract: In 2003, we proposed the hypersystemizing theory of autism. The theory proposes that the human mind possesses a systemizing mechanism (SM) that helps identify lawful regularities (often causal) that govern the input-operation-output workings of a system. ...

Abstract	In 2003, we proposed the hypersystemizing theory of autism. The theory proposes that the human mind possesses a systemizing mechanism (SM) that helps identify lawful regularities (often causal) that govern the input-operation-output workings of a system. The SM can be tuned to different levels, from low to high, with a normal distribution of individual differences in how strongly people search for such input-operation-out-put regularities in any data that is systemizable. Evidence suggests that people with autism are on average hypersystemizers, scoring higher than average on the systemizing quotient and on performance tests of systemizing. In this article, we consider the neural basis behind the SM, since there has been little consideration of the brain basis of systemizing. Finally, we discuss directions for future work in this field.
MeSH term(s)	Autistic Disorder/complications ; Autistic Disorder/pathology ; Brain/pathology ; Brain Mapping ; Cognition Disorders/etiology ; Humans
Language	English
Publishing date	2018-02-15
Publishing country	France
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2188781-0
ISSN	1958-5969 ; 1294-8322
ISSN (online)	1958-5969
ISSN	1294-8322
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Book ; Online: reval

Landi, Isotta / Mandelli, Veronica / Lombardo, Michael V.

a Python package to determine best clustering solutions with stability-based relative clustering validation

2020

Abstract: Determining the best partition for a dataset can be a challenging task because of 1) the lack of a priori information within an unsupervised learning framework; and 2) the absence of a unique clustering validation approach to evaluate clustering ... ...

Abstract	Determining the best partition for a dataset can be a challenging task because of 1) the lack of a priori information within an unsupervised learning framework; and 2) the absence of a unique clustering validation approach to evaluate clustering solutions. Here we present reval: a Python package that leverages stability-based relative clustering validation methods to determine best clustering solutions as the ones that best generalize to unseen data. Statistical software, both in R and Python, usually rely on internal validation metrics, such as silhouette, to select the number of clusters that best fits the data. Meanwhile, open-source software solutions that easily implement relative clustering techniques are lacking. Internal validation methods exploit characteristics of the data itself to produce a result, whereas relative approaches attempt to leverage the unknown underlying distribution of data points looking for generalizable and replicable results. The implementation of relative validation methods can further the theory of clustering by enriching the already available methods that can be used to investigate clustering results in different situations and for different data distributions. This work aims at contributing to this effort by developing a stability-based method that selects the best clustering solution as the one that replicates, via supervised learning, on unseen subsets of data. The package works with multiple clustering and classification algorithms, hence allowing both the automatization of the labeling process and the assessment of the stability of different clustering mechanisms.
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006
Publishing date	2020-08-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Atypical functional connectivity of temporal cortex with precuneus and visual regions may be an early-age signature of ASD.

Xiao, Yaqiong / Wen, Teresa H / Kupis, Lauren / Eyler, Lisa T / Taluja, Vani / Troxel, Jaden / Goel, Disha / Lombardo, Michael V / Pierce, Karen / Courchesne, Eric

Molecular autism

2023 Volume 14, Issue 1, Page(s) 11

Abstract: Background: Social and language abilities are closely intertwined during early typical development. In autism spectrum disorder (ASD), however, deficits in social and language development are early-age core symptoms. We previously reported that superior ...

Abstract	Background: Social and language abilities are closely intertwined during early typical development. In autism spectrum disorder (ASD), however, deficits in social and language development are early-age core symptoms. We previously reported that superior temporal cortex, a well-established social and language region, shows reduced activation to social affective speech in ASD toddlers; however, the atypical cortical connectivity that accompanies this deviance remains unknown. Methods: We collected clinical, eye tracking, and resting-state fMRI data from 86 ASD and non-ASD subjects (mean age 2.3 ± 0.7 years). Functional connectivity of left and right superior temporal regions with other cortical regions and correlations between this connectivity and each child's social and language abilities were examined. Results: While there was no group difference in functional connectivity, the connectivity between superior temporal cortex and frontal and parietal regions was significantly correlated with language, communication, and social abilities in non-ASD subjects, but these effects were absent in ASD subjects. Instead, ASD subjects, regardless of different social or nonsocial visual preferences, showed atypical correlations between temporal-visual region connectivity and communication ability (r(49) = 0.55, p < 0.001) and between temporal-precuneus connectivity and expressive language ability (r(49) = 0.58, p < 0.001). Limitations: The distinct connectivity-behavior correlation patterns may be related to different developmental stages in ASD and non-ASD subjects. The use of a prior 2-year-old template for spatial normalization may not be optimal for a few subjects beyond this age range. Conclusions: Superior temporal cortex is known to have reduced activation to social affective speech in ASD at early ages, and here we find in ASD toddlers that it also has atypical connectivity with visual and precuneus cortices that is correlated with communication and language ability, a pattern not seen in non-ASD toddlers. This atypicality may be an early-age signature of ASD that also explains why the disorder has deviant early language and social development. Given that these atypical connectivity patterns are also present in older individuals with ASD, we conclude these atypical connectivity patterns persist across age and may explain why successful interventions targeting language and social skills at all ages in ASD are so difficult to achieve.
MeSH term(s)	Humans ; Aged ; Infant ; Child, Preschool ; Autism Spectrum Disorder ; Brain ; Brain Mapping ; Temporal Lobe ; Magnetic Resonance Imaging ; Parietal Lobe ; Neural Pathways
Language	English
Publishing date	2023-03-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2540930-X
ISSN	2040-2392 ; 2040-2392
ISSN (online)	2040-2392
ISSN	2040-2392
DOI	10.1186/s13229-023-00543-8
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Article ; Online: Is there an association between prenatal testosterone and autistic traits in adolescents?

Dooley, Niamh / Ruigrok, Amber / Holt, Rosemary / Allison, Carrie / Tsompanidis, Alexandros / Waldman, Jack / Auyeung, Bonnie / Lombardo, Michael V / Baron-Cohen, Simon

Psychoneuroendocrinology

2021 Volume 136, Page(s) 105623

Abstract: Prenatal testosterone (pT) is a crucial component in physiological masculinization in humans. In line with the Prenatal Sex Steroid Theory of autism, some studies have found a positive correlation between pT and autistic traits in childhood. However, ... ...

Abstract	Prenatal testosterone (pT) is a crucial component in physiological masculinization in humans. In line with the Prenatal Sex Steroid Theory of autism, some studies have found a positive correlation between pT and autistic traits in childhood. However, effects in adolescence have not been explored. Hormonal and environmental changes occurring during puberty may alter the strength or the nature of prenatal effects on autistic traits. The current study examines if pT relates to autistic traits in a non-clinical sample of adolescents and young adults (N = 97, 170 observations; age 13-21 years old). It also explores pT interactions with pubertal stage and timing. PT concentrations were measured from amniotic fluid extracted in the 2nd trimester of gestation via amniocentesis conducted for clinical purposes. Autistic traits were measured by self- and parent-reports on the Autism Spectrum Quotient (AQ) which provides a total score and 5 sub-scores (social skills, communication, imagination, attention switching and attention to detail). Self-reported pubertal stage was regressed on age to provide a measure of relative timing. We found no statistical evidence for a direct association between pT and autistic traits in this adolescent sample (males, females or full sample). Exploratory analyses suggested that pT correlated positively with autistic traits in adolescents with earlier puberty-onset, but statistical robustness of this finding was limited. Further exploratory post-hoc tests suggested the pT-by-pubertal timing interaction was stronger in males relative to females, in self-reported compared to parent-reported AQ and specifically for social traits. These findings require replication in larger samples. Findings have implications for understanding the effects of pT on human behavior, specifically existence of effects in adolescence.
MeSH term(s)	Adolescent ; Adult ; Autism Spectrum Disorder ; Autistic Disorder ; Female ; Gonadal Steroid Hormones ; Humans ; Male ; Pregnancy ; Self Report ; Social Skills ; Testosterone ; Young Adult
Chemical Substances	Gonadal Steroid Hormones ; Testosterone (3XMK78S47O)
Language	English
Publishing date	2021-12-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	197636-9
ISSN	1873-3360 ; 0306-4530
ISSN (online)	1873-3360
ISSN	0306-4530
DOI	10.1016/j.psyneuen.2021.105623
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Article ; Online: Hierarchical cortical transcriptome disorganization in autism.

Lombardo, Michael V / Courchesne, Eric / Lewis, Nathan E / Pramparo, Tiziano

Molecular autism

2017 Volume 8, Page(s) 29

Abstract: Background: Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT ... ...

Abstract	Background: Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT signaling, cortical patterning and differentiation) potentially involved in ASD brain abnormalities during childhood and adulthood. However, it remains unclear whether such diverse dysregulated pathways are independent of each other or instead reflect coordinated hierarchical systems-level pathology. Methods: Two ASD cortical transcriptome datasets were re-analyzed using consensus weighted gene co-expression network analysis (WGCNA) to identify common co-expression modules across datasets. Linear mixed-effect models and Bayesian replication statistics were used to identify replicable differentially expressed modules. Eigengene network analysis was then utilized to identify between-group differences in how co-expression modules interact and cluster into hierarchical meta-modular organization. Protein-protein interaction analyses were also used to determine whether dysregulated co-expression modules show enhanced interactions. Results: We find replicable evidence for 10 gene co-expression modules that are differentially expressed in ASD cortex. Rather than being independent non-interacting sources of pathology, these dysregulated co-expression modules work in synergy and physically interact at the protein level. These systems-level transcriptional signals are characterized by downregulation of synaptic processes coordinated with upregulation of immune/inflammation, response to other organism, catabolism, viral processes, translation, protein targeting and localization, cell proliferation, and vasculature development. Hierarchical organization of meta-modules (clusters of highly correlated modules) is also highly affected in ASD. Conclusions: These findings highlight that dysregulation of the ASD cortical transcriptome is characterized by the dysregulation of multiple coordinated transcriptional programs producing synergistic systems-level effects that cannot be fully appreciated by studying the individual component biological processes in isolation.
Language	English
Publishing date	2017
Publishing country	England
Document type	Journal Article
ZDB-ID	2540930-X
ISSN	2040-2392 ; 2040-2392
ISSN (online)	2040-2392
ISSN	2040-2392
DOI	10.1186/s13229-017-0147-7
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