4/2/24: The Brain’s Many Definitions
Author: Paula Argueta | Program Coordinator in the CBrA Lab at The University of Alabama at Birmingham
At our lab meeting on April 2nd, 2024, Program Coordinator, Lucas Martin, and undergraduate researcher, Julia Dominguez presented on the paper “The Brain is…”: A Survey of The Brain’s Many Definitions by Bolt & Uddin (2023). The aim of the paper was to report on the most common phrases of "the brain is...." in biomedical peer-reviewed literature. Lucas and Julia started their presentation with a discussion on the views of the brain over time. Ancient Egyptian, Mesopotamian, and Chinese cultures believed the heart was the source of intelligence, although evidence suggests there was knowledge of brain anatomy, strokes, paralysis, and aphasia. In the 6th/5th cent. BCE, Alcmaeon of Croton claimed the brain is responsible for the senses and intelligence. On the other hand, Aristotle believed the heart to be the source of intelligence. For centuries, Greek philosophers continued to debate the heart vs. the brain as the source of intelligence. In 1848, the case of Phineas Gage's survival of a traumatic brain injury and his reported personality changes highlighted the specialized and functional areas of the brain. Throughout the 1900's Nobel Prizes were awarded to notable scientist for their work on neuron identification and function. Neuroscientists of different specialties are still at work present day using advanced technology to further enhance our understanding of the brain. While the heart is an organ that can be defined pedagogically, the brain is not so readily defined. In order to understand the diversity of definitions that scientist use to define the brain, the authors of the article conducted a survey of biomedical literature using natural language processing techniques (NLP). Using the NLP technique and a custom rule-based algorithm, the authors removed irrelevant expressions, isolated groups with similar meanings, and identified over a dozen commonly used expressions to describe the brain. The commonly used phrases span different levels of organization and different types of expression: the brain is ‘an energy demanding organ’ (N = 426), ‘a complex network’ (N = 412), ‘a heterogenous organ (N = 248), ‘a control system’ (N = 160), and ‘an immune-privileged organ’ (N = 157). This identified three main ways of talking about the brain 1) metaphorically: 'the brain is a prediction machine,' 2) descriptor of general functioning: 'organ that is adaptive to stress,' and 3) properties of the brain: 'highly vascularized organ.' The results of this article identify the diversity with which the brain is understood. Such diversity raises a question of implication for scientific communication. Are we hindered by the lack of uniformity, or do variable approaches open more doors for scientific exploration? In scientific literature, the definition of the brain used in a study serves as a justification for the research aim and perspective. While there is variability defining the brain, the authors re-emphasize a common expression that they agree with, which is that the brain is the most complex organ of the human body. This complexity is not only seen in its biological structure and function, but also in the ways in which scientists understand and describe it. Our lab discussed the aims and findings of this article in the context of the definition of Autism Spectrum Disorder (ASD). Would there be a similar variability of the definition of ASD if a similar analysis was done? We discussed that most of the literature we read that defines ASD cites the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), created by the American Psychiatric Association. Perhaps we would not see much variability, as the citation offers a more accurate definition of ASD while also being used for diagnostic purposes.
Bolt, T., & Uddin, L. Q. (2023). "The brain is...": A survey of the brain's many definitions. Pre-print. https://doi.org/10.1101/2023.10.26.564205
2/27/24: Biological Role of BOLD Signal Variability
Author: Josie Zachman | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
At our lab meeting on February 27th, 2024, undergraduate researcher Daphne Rivera and Postdoctoral researcher Dr. Elizabeth Valles-Capetillo presented and explored the paper “The biological role of local and global fMRI BOLD signal variability in human brain organization” (Baracchini et al., 2023). This paper was extremely in-depth, covering many different modes of analysis and systemization of data. This paper is not yet published, but was made available as a pre-print in October 2023. It is very recent and uses groundbreaking analysis methodology to construct a detailed functional understanding of the brain. Certain main points include the use of fMRI data to assess BOLD (blood-oxygenation level dependency) signal variability in two different ways– local and global. They outline the use of local versus global variability, with local temporally assessing BOLD signal variability in a particular region, and the global temporally assessing BOLD signal variability in multiple regions within a network. The authors’ aims of this analysis included examining different types of variability within the brain, assessing the reliability of their findings, comparing BOLD variability with topographical and neurobiological properties at the micro, meso and macrostructural level, and mechanically understanding the nature of the differences in variability. To quantify and assess local signal variability, the authors performed a root mean squared successive difference (rMSSD) for each of the normalized regions. The greater the rMSSD, the greater the local BOLD variability. Global signal variability was slightly more complicated, incorporating several different analysis methods such as covSTATIS, NxNxT, NxN, and evaluating multivariate connectivity space. For these measures, the greater the Hull Area, the greater the global variability. Some major results include the finding that BOLD signal variability was reduced with age. They found that local variability was more highly associated with sensory areas and that global variability was more highly associated with cortical association regions. Overall findings about the inhibitory signature of the BOLD variability match existing literature. With many points of discussion, lab members provided remarks on how BOLD variability could be affected, as well as some other potential limitations of the study (such as the potential use of different MRI scanners). Above all, discussions took place about the relevance of this information to our current lab practices and studies, and how we may be able to incorporate certain methodologies of their work to provide better data. Overall, the presentation was well-done and informative, and the research that was conducted by these authors is important to the field of functional neuroscience.
Baracchini, G., Zhou, Y., Castanheira, J. da S., Hansen, J. Y., Rieck, J., Turner, G. R.,... & Spreng, R. N. (2023). The biological role of local and global fMRI BOLD signal variability in human brain organization (p. 2023.10.22.563476). bioRxiv. https://doi.org/10.1101/2023.10.22.563476.
2/13/24: Neural Correlates of Visual Imagery Vividness
Author: Lucas Martin | Project Coordinator in the CBrA Lab at The University of Alabama at Birmingham
During the CBrA Lab meeting on Feb 13, project coordinator Paula Argueta presented the paper “The neural correlates of visual imagery vividness– An fMRI study and literature review” by Fulford et al. (2018). Visual imagery refers to a mental image produced when thinking of a concept, or the “mind’s eye”. The vividness of this visual imagery varies by individual. The inability to form visual imagery is referred to as aphantasia, and is a relatively new and small area of study. Several cognitive functions are thought to go into visual imagery– executive processes, memory processes, and quasi-perceptual processes. The present paper was inspired by the clinical case of MX, reported by the same authors. MX was a patient who underwent a cardiac procedure, after which he lost the ability to consciously experience mental imagery– his dreams had no images in them and he couldn’t produce visual imagery while awake anymore. fMRI results indicated hypoactivation of the fusiform gyri (FFG) and other temporo-occipital regions, as well as hyperactivation of anterior regions, particularly the right anterior cingulate cortex (rACC). This case led to the naming of this condition as “aphantasia”. The present sample included 14 high-vividness visualizers and 15 low-vividness visualizers, as determined by the Vividness of Visual Imagery Questionnaire (VVIQ). Participants underwent an fMRI scan with a visualization task where they were shown sequences of images and text with instructions to perceive and imagine. Participants were shown the following sequence of images and text: 1) a photo of a famous face/place (Perception), 2) text naming the famous face/place (Imagery), 3) fixation cross, 4) blurred and inverted photo of the famous face/place (Perception Control), 5) random string of text (Imagery Control), 6) fixation cross. After the entire fMRI protocol, participants are shown the photos outside of the scanner and asked to rate the vividness of their visual imagery for each stimulus. Results from the fMRI task showed that among the low-vividness group, numerous brain regions across both hemispheres had significantly higher activation during Imagination than the high-vividness group. By contrast, only regions in the medial frontal lobe and insula were activated more strongly during imagination than the low-vividness group. During Perception, no brain regions had higher activation in the high-vividness group than the low-vividness group, but one small cluster in the Middle Occipital Gyrus (MOG) was activated more strongly in the low-vividness group compared to the high-vividness group. When looking at reported imagery vividness, posterior brain regions extending from the occipital to the parietal and temporal lobes exhibited a positive correlation with vividness, including: Superior Occipital Gyrus (SOG), Superior and Middle Temporal Gyri (STG & MTG), Precuneus, Posterior Cingulate, FFG, and Parahippocampal Gyrus (PHG). These regions are associated with higher order visual processing, semantic processing, visual spatial imagery, episodic memory, spatial processing, face perception, and spatial memory. A contrasting set of regions was found to have a negative correlation with vividness, including: Cuneus, Inferior Occipital Gyrus (IOG), MOG, Precentral and Inferior Frontal Gyri (PRFG & IFG), Insula, and Anterior Cingulate. These areas are mostly associated with executive functions, audition, and semantic memory. Widespread activation in the low-vividness group could reflect a failure to suppress activity interfering with vividness (such as in the auditory cortex), or it could reflect compensatory activation of executive regions to drive vividness. Additionally, it could be due to a difference in strategy for visualization (i.e., pulling from non-visual sensory sources to imagine a concept). Our lab members discussed these findings in context of our ongoing clinical trial study, BrainREAD. BrainREAD involves a reading comprehension intervention that aims to improve comprehension through the use of visual imagery as a mechanism to retain information. Theoretically, improvement in concept imagery skills could improve memory, receptive language, planning and organization, etc., in addition to reading comprehension. We also briefly discussed the use of exogenous psychedelic drugs to improve visual imagery in the context of past research, as well as the use of hallucinogens in past research to treat psychiatric disorders.
Fulford, J., Milton, F., Salas, D., Smith, A., Simler, A., Winlove, C., & Zeman, A. (2018). The neural correlates of visual imagery vividness – An fMRI study and literature review. Cortex, 105, 26–40. https://doi.org/10.1016/j.cortex.2017.09.014
11/8/23: Diverging Asymmetry of Intrinsic Functional Organization in Autism
Authors: Caroline Vanderburgh & Katelyn Woolbright | Undergraduate Research Assistants in the CBrA Lab at The University of Alabama
At our lab meeting on 11/8, undergraduate researcher Ashton Daum and post-doctoral researcher Dr. Elizabeth Valles-Capetillo presented the paper “Diverging asymmetry of intrinsic functional organization in autism” written by Wan et al. published in 2023. This paper aimed to study system-level hemispheric imbalances in autism and examine autism-specific deviances in functional lateralization. The hypothesis was that atypical lateralization axes in autism may contribute to autistic behaviors. The study used fMRI ABIDE data from 283 male participants. The fMRI data on the whole brain was split into 12 functional networks and preprocessed using standard measures and subjected to slice-time corrections, head motion corrections, skull stripping, and intensity normalization. Researchers analyzed data for asymmetry, developmental effects, heritability impacts, and phenotypic impacts. When comparing the asymmetry of functional gradients between autistic and non-autistic individuals, a similar pattern of functional organization was found in both groups as compared to the Human Connectome Project. To investigate if asymmetry between functional gradients develops differently in autistic compared to non-autistic individuals, participants were split into three age groups. In the non-autistic groups, a shift in leftward lateralization occurs as the brain develops. This could perhaps be indicative of language skill development. In the autistic group, no change in lateralization occurred with age, which could explain difficulty in language and sensory processing. Furthermore, various regions in the inter-hemisphere found to be heritable presented differently in the autistic group, suggesting that there is a genetic component to autism. Finally, it was found that interhemispheric asymmetry features could account for 13% (r=.13) of communication differences and 15% (r=.15) of social differences as measured by the ADOS. As our lab concluded our insightful discussion and assessment of this article presented by Elizabeth and Ashton, several implications of this study remained. One inherently crucial limitation that was evaluated was the manner in which atlases were not analyzed within different experimental systems, but rather limited their analysis to one particular system in an attempt to reach a larger audience. Specifically to this research article, the HPC (High-Performance Computing) atlas was highly utilized comparatively to those available, as the benefits and efficiency corresponded more recognizably to the matter given, yet various others were used designated to a specific component of conten. One of the last limitations that our lab verbally discussed following this was the essence of the question, “Which atlas should we use?”. This was highly challenging to address considering the retrospect to autism and autistic behaviors, with further regard to the neurological and neurobiological realm concerning ASD. Given the multitude of the amount of atlases available, being able to cover all bases concerning this analysis would have been nearly impossible. Results concluded through the use of several atlases, influences of right and left hemisphere asymmetry, developmental effects, hereditability, and phenotypic proposition may contribute towards atypical lateralization axes in autism, which inherently may contribute to autistic behaviors. Through the conclusive stance of the article, it was emphasized that the utilization of more than one atlas may contribute to conflicting results. Our lab discussed the premise in which we could improve atlas publication and efficiency as these resources become more readily available. Furthermore, the study at hand presented the vitality that demonstrates the critical need to evaluate the reproducibility of neuroscience research with atlas use. The advancement of this technology will be tremendously beneficial, as autism research will greatly benefit, and this study’s findings may be readily reproduced to discover more correlations between neurological data and ASD.
Wan, B., Hong, S. J., Bethlehem, R. A. I., Floris, D. L., Bernhardt, B. C., & Valk, S. L. (2023). Diverging asymmetry of intrinsic functional organization in autism. Molecular Psychiatry, 28, 4331-4341. https://doi.org/10.1038/s41380-023-02220-x
11/1/23: White Matter Alteration in ADHD
Author: Julia Kosienski | Undergradute Research Assistant in the CBrA Lab at The University of Alabama
At our lab meeting this week, undergraduate researchers Julia and Josie presented the following paper, “White matter alterations in Attention-Deficit/Hyperactivity Disorder (ADHD)” by Parlatini and colleagues (2023). Before our presenters started their presentation, they added a disclaimer that explained this paper was very jargon heavy and it was a difficult study to comprehend. Despite this paper being difficult, our undergraduate researchers did an amazing job. To begin, the history of research on ADHD is consistent, but limited in the scope of age differences, and the exclusion of studies with techniques not amenable to meta-analysis. Also, because advancements in diffusion weighted imaging technology have been made since the start of research in this field, a new and broader meta-analysis was needed. This study, performed an assessment based on imaging acquisition, preprocessing, and analysis. They used signed differential mapping, and meta-analyzed a group of the retrieved studies compliant to quantitative evidence synthesis in individuals of any age and in children, adults, and high-quality datasets. Finally, this study conducted meta-regressions to test the effect of age, sex, and medication-naivety. Our lab broke down what ADHD and DWI (diffusion weighted imaging) is to make this jargon filled study a bit more manageable for everyone. We discussed ADHD is a common neurodevelopmental disorder that is marked by three symptoms: inattention, hyperactivity, and impulsivity. We also learned Diffusion Weighted Imaging (DWI) is a MRI technique that allows for the extraction of quantitative indexes of white matter microstructural organization. It allows for the examination of the diffusion of water through white matter layers. Our presenters also took the time to break down many parts of the brain that were included in this study to help the lab have a better understanding. The results of the meta-analysis and meta-regression show that in adults with ADHD, there was a reduced Fractional Anisotropy (FA) in splenium of the corpus callosum. This matches the studies role in supporting cognitive and motor functions affected in ADHD, as well as disrupting their interaction with the DMN. Meta-regression results found the splenium and the body of corpus callosum reduced FA differences was amplified with age. There is reduced FA in several networks/pathways that, when impaired, are related to inattention, impulsivity, executive dysfunction, ADHD symptom severity, and emotion dysregulation. Meta-analyses found reduced FA values in adult individuals with ADHD and reduced FA in the corpus callosum with advanced age. The lab discussed whether research should be accessible to the public in terms of clarity and understanding. Our lab believes that published research should be accessible to both those within and outside of academia. Although, if research was easier to access, it could lead to people making incorrect assumptions or incorrectly understanding what the data and research is attempting to communicate. Also, it may not be possible to make a topic that is complicated and uses big vocabulary words any easier. This type of study was hard because there are big vocabulary words, but many cannot be broken down anymore. It may be more of a matter of the reader needing to do independent research on words like diffusion weighted imaging and fractional anisotropy to be able to understand the study. While we would love to make research accessible to all, we also are aware of the roadblocks that hinder accessibility.
Parlatini, V., Itahashi, T., Lee, Y., Liu, S., Nguyen, T. T., Aoki, Y. Y., Forkel, S. J., Catani, M., Rubia, K., Zhou, J. H., Murphy, D. G., & Cortese, S. (2023). White matter alterations in Attention-Deficit/Hyperactivity Disorder (ADHD): A systematic review of 129 diffusion imaging studies with meta-analysis. Molecular Psychiatry, 28, 4098–4123. https://doi.org/10.1038/s41380-023-02173-1
10/11/23: Eye-Tracking Diagnosis of Autism
Author: Lily Savage | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
At our lab meeting this week, undergraduate researchers Caroline and Julia K. and graduate researcher Meagan, presented the following paper, “Eye-tracking-based measurement of social visual engagement compared with expert clinical diagnosis of autism” by Jones et al., 2023. The aim of the study was to evaluate the performance of eye-tracking in measuring how children look at and learn from their environments. The importance of this study is rooted in the desire to diagnose ASD while the brain is at its most malleable and before maladaptive behaviors become very difficult to diagnose, so the earlier in life the better. At this point in time, most ASD diagnoses are made based on behavioral indicators and sometimes come after up to a year of delay has already occurred. Eye-tracking is seen as being more efficient than neuropsychological measures and tests and can be quantified quicker and easier, potentially leading to ASD diagnoses being made earlier. It should be added that the researchers were not aiming for eye-tracking to replace clinical tests like the ADOS, but it can be used to further confirm a diagnosis or identify earlier markers. Jones et al.’s study took place across 6 sites, included 475 completed measurements of eye-tracking in 16-30 month-year-olds already displaying autistic traits, and was both a double-blind and within-subjects experiment. It was found that the eye-tracking measurement was successful in gauging social visual engagement in 95.2% of participants, but the uncertainty of such was 29.5%, which is relatively high. The study also yielded a 71% sensitivity rate (eye-tracking techniques correctly predicted 71% of autistic children out of the total number of autistic children in the sample) and an 80.7% specificity rate (correctly predicted 80.7% of non-autistic children out of the total number of non-autistic children in the sample). Thus, eye-tracking may be better at identifying non-autistic children than it is at definitively identifying autistic children. There were no significant findings regarding race, but Hispanic and Black/African American children were given more ASD diagnoses than white children were which is surprising given that these populations are often underdiagnosed in non-experimental settings. In the lab’s discussion of the study, certain limitations were identified. The findings of this paper did not attempt to explain any sex differences in ASD, which often ends up being a significant factor in diagnosis; males are diagnosed earlier and more frequently than females. Cultural differences were also not addressed, therefore these findings may not be intended to represent the general population. Regardless, the findings of Jones et al.’s study still demonstrate that eye-tracking can be a great supplemental tool in ASD diagnosis. As more biomarkers of ASD are correctly and efficiently identified, the less room underlying bias has in diagnosis.
Jones, W., Klaiman, C., Richardson, S., Aoki, C., Smith, C., Minjarez, M., Bernier, R., Pedapati, E., Bishop, S., Ence, W., Wainer, A., Moriuchi, J., Tay, S.-W., & Klin, A. (2023). Eye-Tracking–Based Measurement of Social Visual Engagement Compared With Expert Clinical Diagnosis of Autism. JAMA, 330(9), 854–865. https://doi.org/10.1001/jama.2023.13295
9/27/23: Brain & Language Associations in ASD
Authors: Ashton Daum & Julia Dominguez | Undergraduate Research Assistants in the CBrA Lab at The University of Alabama
On September 27th, the CBrA lab discussed “Brain and Language Associations in ASD; A Scoping Review,” by Cermak et al., (2021), presented by undergraduate researchers Daphne Rivera and Molly Hart. This publication analyzed the current literature regarding brain structure and language impairment across 17 studies, 13 of which were conducted in the U.S. In total, 3854 language exam records were identified, screened, and subsequently had duplicates and disqualifying reports (those that did not include a typically developing (TD) control group, utilized parent report, had MEG or EEG measurement, did not complete analyses tests, or was not an empirical research study) removed. The remaining 17 studies include both Autism Spectrum Disorder (ASD) and TD participants, utilized a standard measure of language, had an MRI measurement, and included at least 10 participants. Our guiding question was, what brain regions are associated with language performance in individuals with ASD? Specific answers remain elusive, as analyzes of several components of the brain revealed no singular immediately obvious links between brain structure and subsequent language performance. A particularly notable finding was that when presented with a challenging language task, the right side of the brain exhibited greater stimulation, when one might expect this effect on the language-associated left hemisphere instead. Certain physical differences were also observed, such as a significantly larger right inferior frontal gyrus among those who possessed a language impairment. Both the authors and presenters noted several distinct limitations of the scoping review, most notably the total lack of female participants in 12 of the 17 studies, the utilization of “total language” scores as opposed to subsets, and very few of the studies including a group with both an ASD and a LI (language impairment) diagnosis. In light of these potential issues, our conversation was directed to the underrepresentation of female participants in nearly every ASD-focused study. Potential reasons discussed included the socialization of young girls to be more mature socially, and the increased need to mask, or disguise autism symptoms, leading to later diagnoses. This review covered vast quantities of language-related structural brain differences arising from autism, acquired from comprehensive reviews of the current literature. However, further research can be performed to more specifically examine the structural and functional relationship between ASD and language.
Cermak, C. A., Arshinoff, S., Ribeiro de Oliveira, L., Tendera, A., Beal, D. S., Brian, J., Anagnostou, E., & Sanjeevan, T. (2021). Brain and language associations in autism spectrum disorder: A scoping review. Journal of Autism and Developmental Disorders, 52(2), 725–737. https://doi.org/10.1007/s10803-021-04975-0
9/13/13: Pathways to Psychopathology in ASD Adults
Authors: Josie Zachman & Grace McClain | Undergraduate Research Assistants in the CBrA Lab at The University of Alabama
At this week’s lab meeting, Luke and Paula presented a summary of the paper titled “Pathways to Psychopathology Among Autistic Adults” by Susan W. White, Greg J. Siegle, Rajesh Kana, and Emily F. Rothman in 2023. This paper touched on different comorbidities that we may see in adults with Autism Spectrum Disorder (ASD) and how the risk factors for these psychiatric comorbidities can be sorted into three categories: affective, cognitive, and social. Luke and Paula also presented information on the Social Model of Autism and the Medical Model of Autism. The Social Model of Autism reflects the idea that an autistic individual faces barriers in the form of a society that is not always accommodating or understanding. The medical model represents the idea of finding a cure or overall symptom relief for autistic individuals with a focus on fixing the downsides of “typical” impairment. The purpose of this paper was to establish a link between ASD and mental health disorders to help inform the efforts of possible intervention and identifying risk factors. Autistic individuals may experience affective psychiatric symptomology in ways such as alexithymia (defined as an impaired ability to identify and report on, or describe one’s own emotional state), emotional disregulation, or experiencing autistic inertia (getting stuck in a mindset and being unable to apply coping strategies). Conversely, some cognitive risk factors can be viewed as related to cognitive load and executive function. Examples of these are seen in functional brain differences between sensory and motor processing in ASD and NT. Lastly, social risk factors can be seen in examples of autistic masking, social motivation differences, and other societal factors. The social motivation hypothesis was explained, although it is highly contested. The group discussion of this topic focused on how we can address risks and barriers that autistic individuals experience in different aspects of the three categories. Many lab members agreed that a significant step to take would be to inform and have a greater understanding amongst neurotypical populations. Both societal and individual changes can be made to create more accommodating areas and social situations for autistic people and other individuals. We also discussed the pros and cons of the Social Model of Autism and the Medical Model of Autism as well as the effect they have on autistic people. It was agreed in the discussion that the extremes of both models were harmful and that the best compromise would be a blend of the two theories. Lastly, Paula and Luke presented preliminary ideas for a new lab project focusing on neurodiversity. Similar to the BrainREAD project, this project will use fMRI data along with psychometric measures.
White, S. W., Siegle, G. J., Kana, R., & Rothman, E. F. (2023). Pathways to psychopathology among autistic adults. Current Psychiatry Reports, 25(8), 315–325. https://doi.org/10.1007/s11920-023-01429-5
4/14/23: Language & Terminology in ASD
Author: Lily Savage | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
At our lab meeting this week, undergraduate researchers Ashton Daum and Katelyn Woolbright presented on the following paper, “Exploring an e-learning community’s response to the language and terminology use in autism from two massive online courses on autism education and technology use” by Lei et al., 2021. The aim of the study was to investigate how people in the autism community, as well as educators and family members, feel about the terminology used to identify individuals with ASD diagnoses. The current debate cited by the literature is identity-first language (‘autistic individual’) versus person-first language (‘individual with autism'). As reported by the researchers, identity-first language was previously thought to be favored by the actual autistic community and used in more casual settings while person-first language was favored by healthcare professionals and clinicians vastly. The study design set forth by Lei et al., 2021 was not designated to pick a “correct” answer but to simply explore how backgrounds, beliefs, and knowledge contribute to which term people tend to utilize or prefer. The study took place across 4 weeks in 2 online courses where autism was the subject matter. Participants were asked to utilize discussion forums throughout the class where the discussion on the types of terminology used was monitored by the researchers. The 803 participants in the classes and in the discussions were self-advocates, friends, family members, and professionals in the autism community. It was ultimately found that most participants indicated no clear or significant preference regarding the appropriate terminology used to refer to autistic individuals; it was deemed best on most occasions to simply ask the person one is referring to how they prefer to be referred to. By educating oneself on the literature on autism terminology, it becomes easier to access and support resources and accommodations, communicate with individuals regarding their strengths and weaknesses, and recognize that autism forms a core part of an individual’s identity. This presentation guided productive conversation in our lab surrounding the discussion of autism terminology. We debated whether language discussion is a distraction and like, Lei et al., 2021’s study results, agreed that terminology should be guided by the individuals it is about. It only takes a moment to ask someone what their preference is. We also discussed how the autistic community can be engaged in more meaningful research and practice, which begins with the inclusion of these individuals and the respect of their preferences in the labs themselves.
Lei, J., Jones, L., & Brosnan, M. (2021). Exploring an e-learning community’s response to the language and terminology use in autism from two massive open online courses on autism education and technology use. Autism, 25(5), 1349-1367. https://doi.org/10.1177/1362361320987963
3/24/23: Brain Connectomics and SAD
Author: Katelyn Woolbright | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
For the CBrA lab meeting held on March 24, researchers Mckayla Kurtz and Julia Kosienski presented the paper, “Brain connectomics predict response to treatment in social anxiety disorder” by Whitfield et al. in 2016. As emphasized through the presentation and the article itself, limited amounts of scientific evidence provide optimal forms of treatment for individual patients with neuropsychiatric disorders, specifically in reference to social anxiety disorder (SAD). The most prevalent forms of treatment include cognitive behavioral therapy (CBT) and pharmacotherapy, which are moderately effective and still leave an array of patients who remain symptomatic. This study seeks to examine if brain connectomics can accurately predict therapeutic response to CBT treatment in patients with SAD through the use of resting-state functional magnetic resonance imaging (rsfMRI) and diffusion-weighted magnetic resonance imaging (dMRI). Brain connectomics incorporates the components of the intrinsic functional and structural organization of the brain. With the use of rsfMRI and dMRI, the ability to detect distinguishing information concerning brain activity in coherence CBT treatment became readily visible. The article indicated that the use of both imaging techniques was successful in predicting the response to cognitive behavioral therapy (CBT) treatment for those with SAD significantly better than the clinician-measured level of severity, as clarified by approximately 80%. These findings provide substantial details on which connectomics-based neuromarkers may provide an outlet for other neuropsychiatric disorders. With the effectiveness of connectomics, our lab discussed the possibility of how this might be used within our studies, specifically in coherence with autism spectrum disorder (ASD). Overall, this article provided our lab with a fruitful discussion concerning the constant growth of the neuroimaging field. The persistent growth of this field has provided analytical discussion revolving around the future of neuropsychiatric diagnoses, an effective form of treatment designed based on the individuality of a patient, and the potential prevention of severe outcomes for individuals
Whitfield-Gabrieli, S., Ghosh, S. S., Nieto-Castanon, A., Saygin, Z., Doehrmann, O., Chai, X. J., ... Gabrieli, J. D. E. (2016). Brain connectomics predict response to treatment in social anxiety disorder. Molecular Psychiatry, 21, 680-685. https://doi.org/10.1038/mp.2015.109
3/10/23: DMN in ASD & ADHD
Author: Daphne Rivera | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
For this lab meeting, we discussed the paper “A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder” by Amritha Harikumar et al. in 2021. The Default Mode Network (DMN) consists of the posterior cingulate cortex (PCC), anterior cingulate cortex (ACC), medial prefrontal cortex (mPFC), medial temporal lobes (MTL), angular gyrus (AG), and the precuneus. It is primarily active when an individual is at rest and is not actively engaged in any specific task. Because of the nature of the DMN, its activity is often measured during resting fMRI scans, in which an individual is asked to sit in the scanner and try not to think about anything in particular. After establishing a baseline of what DMN is and how it is measured in research, we moved to discussing the current research findings on how the DMN changes in autistic individuals and individuals with ADHD. With ASD, there have overall been mixed findings of both overconnectivity and underconnectivity of the DMN in autistic individuals compared to neurotypical individuals. Some people have reported that there is a developmental “switch” where, in childhood, autistic individuals will have hyperconnectivity of the DMN by hypoconnectivity in adulthood. Changes in DMN functional connectivity (FC) have been associated with social communication deficits such that increased FC is correlated to increased social communication deficits. In individuals with ADHD, the research shows a trend of increased FC in the DMN and throughout the brain in individuals with ADHD compared to neurotypical individuals, though the changes in DMN-FC across the lifespan have not been as well delineated. One common theory however is the developmental delay hypothesis, which says that ADHD symptoms are associated with a delay in the maturation of the DMN. Additionally, ADHD has been associated with hyper-connectivity between the dorsal and ventral attention networks. Additionally, we also discussed the commonality of comorbid ASD and ADHD and how this impacts the inclusion criteria for research studies. ASD and ADHD occur together at a high frequency, with 20-50% of children with ADHD also meeting criteria for ASD and 30-80% of children with ASD meeting criteria for ADHD. Often in ASD research, participants are excluded who also meet the criteria for ADHD and vice versa in ADHD research. However, there is evidence that DMN connectivity is unique for individuals who meet diagnostic criteria for both ASD and ADHD. Ultimately, future research can better delineate the role of the DMN in ASD and ADHD by conducting studies that look at comorbid cases of ADHD and ASD as well as individual cases.
Harikumar, A., Evans, D. W., Dougherty, C. C., Carpenter, K. L. H., & Michael, A. M. (2021). A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder. Brain connectivity, 11(4), 253–263. https://doi.org/10.1089/brain.2020.0865
3/3/23: Exclusionary Practices in Neuroscience
Author: Josie Zachman | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
This week in our lab meeting, researchers Skyler, Paula, and Lily examined and presented “Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data” by Ricard et al. in 2022. This paper outlines some of the more important themes of inclusion in neuroscience recruitment and data acquisition that we strive for in the lab. Inclusion practices were broken up into several different aspects, such as in sampling/recruitment, and in methodology/data acquisition. We first went over some historical examples of racism and ethnic superiority, such as craniometry- which was used in the past as a “scientific” way to uphold white superiority. Examples of dangerous and unethical research studies were explained as well, such as the Tuskegee Syphilis Study, The Penicillin Study on Guatemalan citizens, and Henrietta Lacks’ non-consensual cell donation. All of these historical samples give insight into an aspect of why sampling might be so biased against minorities- people may be distrustful of the research community. Some other reasons for biased sampling include language barriers and conducting research only at higher-education facilities. Phenotypic differences between people prove to be difficult within research studies, especially with highly physical tests such as an EEG or an MRI. These tests are typically designed with only non-minority features in mind. One example includes the problems that arise with using EEG electrodes on thicker or coarser hair- the traditional electrodes require adequate contact with the scalp, and are not designed to work between coarser hair. Despite advances being made, such as an EEG “cap” that works just as well with people with cornrows, they are not yet widespread. Many different examples of racially exclusionary practices were discussed in this meeting, as well as how out lab combats these typical practices, and what we can do to promote further diversity. Inclusive processes are very important to having generalizable research and reducing the harm of bias in science.
Ricard, J. A., Parker, T. C., Dhamala, E., Kwasa, J., Allsop, A., & Holmes, A. J. (2022). Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data. Nature Neuroscience, 26, 4-11. https://doi.org/10.1038/s41593-022-01218-y
2/10/23: Neuroscout
Author: Caroline Vanderburgh | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
For the CBrA lab meeting on February 10, post-doctoral researcher Dr. Elizabeth Valles Capetillo and undergraduate researcher Elia Harper presented the paper “Neuroscout, a unified platform for generalizable and reproducible fMRI research” by de la Vega, Rocca, et al. published in 2022. Neuroscout is an accessible program that utilizes machine learning to annotate events in naturalistic stimuli of fMRI studies. A key aspect to scientific research is reproducibility, as it allows other researchers to repeat specific procedures and verify results. One of the main critiques of fMRI research is that it is often neither easily replicable nor reproducible. Neuroscout aims to address this issue by implementing uniform programming using container technology, encouraging re-analysis of public datasets, and standardizing model specifications and automated workflows. Neuroscout also enables the ability to refine variable analysis. A specific case study by Yarkoni, et al. published in 2008 looked at selectivity to word frequency in the visual word form area (VWFA). While this study looked at activation in the VWFA, it was difficult to look at and analyze multiple different variables at once. Researchers were able to utilize Neuroscout to help focus and shift variable analysis and discern more descriptive characteristics, e.g., phonological and orthographic word properties. Towards the end of this presentation, the lab was able to form our own hypothesis and run it through Neuroscout. We decided to focus on facial expressions in the movie The Grand Budapest Hotel and compare a few different variables: music, speech, man, woman, dark, daylight, and pitch. Neuroscout allows us to compare all or some of these variables to each other by creating a contrast and designing a correlation matrix. The container then takes about 20 minutes to display an activation map of these variables. We were all delightfully surprised at how simple and fast it was to use Neuroscout. While Neuroscout is a great platform, we discussed some limitations and potential for improvement. It is impossible to account for all confounds in data-driven media, and Neuroscout has no quality control system in place. Furthermore, Neuroscout should aim to include more datasets as opposed to just naturalistic ones, and more nuanced analyses to expand its application. However, Neuroscout is a promising step in the right direction for fMRI research and will hopefully help improve reproducibility and generalizability standards.
De La Vega, A., Rocca, R., Blair, R. W., Markiewicz, C. J., Mentch, J., Kent, J. D., ...Yarkoni, T. (2022). Neuroscout, a unified platform for generealizable and reproducible fMRI research. eLife, 11:e79277. https://doi.org/10.7554/eLife.79277
1/27/23: ASD From Birth Through Infancy
Author: Julia Kosienski | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
For the CBrA meeting on January 27th, undergraduate researchers Josie Zachman and Molly Hart presented the paper, “Brain and Behavior Development in Autism from Birth Through Infancy” written by Mark D. Shen and Joseph Piven published in 2017. Currently, the autism field uses observed behavioral criteria like motor skills, visual reception, language, and eye gaze patterns to create a diagnosis. This is done at age two or later. Because of the heterogeneity of ASD subtypes and the range of symptomatic severity, the use of behavioral criteria for diagnoses is considered inadequate. The autism field is currently working to find biological markers for an ASD diagnosis to ultimately start intervention sooner than two years of age. Unfortunately, biomarkers are elusive at this current time and have challenges when trying to establish neural markers in infants. The lab discussed that finding a biomarker would be difficult due to the rapid growth of an infant's brain. The brain would continue to change and grow making it hard to find an accurate marker. The research even expressed that at ages two and three, brain size is significantly enlarged for ASD groups. While the lab stated this would be a complex task, it is a task our lab and others need to continue to work towards to be able to have an earlier diagnosis for positive intervention purposes. It was also brought up by a potential facility member that there is new eye tracking technology that has been working towards an earlier diagnosis. She explained at this moment it is only 80% accurate. Again, this reiterates why our research and others is so important to continue to work towards early diagnoses. The lab also discussed that because a child’s brain with ASD grows rapidly, head circumference is seen as a first indicator of ASD. As of now, physicians then tell families to watch out for the behavioral signs. With better access to fMRI scanners, families can have a better understanding of the diagnosis and get intervention sooner. Research has found that cortical surface area increases from six to twelve months and total brain volume increases from twelve to twenty-four months. This tells us that there may be biological predictors before behavioral indicators emerge. It was also noted that an excessive amount of cerebral fluid was linked to the HR-ASD group at six months. Too much cerebral fluid can be harmful to a developing baby. Lastly, abnormalities in white matter were seen as early as six months. Neuroimaging is something our lab is passionate about and believes could have a positive effect when it comes to advancements. The research presented the idea of functional imaging and how it can have a positive effect on observing connectivity in children.
Shen, M. D., & Piven, J. (2017). Brain and behavior in autism from birth through infancy. Dialogues in Clinical Neuroscience, 19(4), 325-333. https://doi.org/10.31887/DCNS.2017.19.4/mshen
12/2/22: Predictive Modeling in Autism
Author: Caroline Vanderburgh | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
For the CBrA meeting on December 2nd, post-doctoral researcher Dr. Elizabeth Valles Capetillo presented the paper, “Functional Connectome-Based Predictive Modeling in Autism,” written by Horien et al. published in 2022. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition categorized by a wide variety of symptoms that are not always clearly defined and can differ depending on the individual. This leads to a lack of empirically validated treatments. Therefore, researchers are aiming to discover new methods of creating models to understand neurological differences in Autism to better predict ASD and develop more effective treatments. In fMRI research, predictive modeling can be used to relate connectivity measures to phenotypic measures. This type of modeling is useful in Autism research as it can provide biological insight and clinical utility. However, there are ASD-specific factors that must be taken into consideration when using predictive modeling: sex differences, age differences, comorbidities, and individual differences. These issues can be addressed with data that has already been obtained by strategically choosing to use one or multiple types of predictive modeling: case-control, dimensional, and subtyping. Case-control modeling focuses on differentiating those with a disease, and those without. Dimensional modeling focuses on analyzing symptoms that exist on a continuum and identifying specific behavioral domains. Subtyping focuses on separating groups based on certain demographics, such as age, sex, etc. Our discussion sparked concern over consistency in ASD research. Different labs may use different diagnostic and inclusion criteria for autistic participants. In our own lab, the process for confirming an ASD diagnosis differs in BrainREAD and MISLEAD. Furthermore, researchers may fail to subtype ASD participants correctly and overlook different neurobiological differences between different groups of participants. Therefore, it is not only the type of model utilized that matters, but also the data that is selected to be included.
Horien, C., Floris, D. L., Greene, A. S., Noble, S., Rolison, M., Tejavibulya, L.,...Constable, R. T. (2022). Functional connectome-based predictive modeling in autism. Biological Psychiatry, 92(8), 626-642. https://doi.org/10.1016/j.biopsych.2022.04.008
10/28/22: Multivoxel Pattern Analysis in fMRI
Author: Luke Martin | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
During the CBrA lab meeting on October 28, post-doctoral researcher Dr. Elizabeth Valles Capetillo presented on Multivoxel Pattern Analysis (MVPA), specifically within the context of the paper “Multivoxel Pattern Analysis in fMRI: a practical introduction for social and affective neuroscientists” by Weaverdyck et al. (2020). MVPA is a style of data analysis used in neuroimaging. As background, a voxel is a unit of 3D space; just as a 2D image has pixels, 3D images (such as those resulting from a MRI scan) are comprised of voxels. In the context of neuroimaging, a voxel refers to a specific location within the brain. When using univariate analysis to examine brain activation, researchers look at whether or not a specific region (voxel) was activated and to what extent (high vs. low). Multivariate analysis can be conducted to examine the relationships between voxels and their activation. MVPA is a form of multivariate analysis that looks at the order and timing of voxel activation. The example of typing on a keyboard was offered: you can look at typing the words “cat” and “dog” as identical from the standpoint of the number of keys pressed (analogous to univariate analysis). However, if you look at the pattern in which the keys were pressed and the location of the keys, the two words are completely different (analogous to MVPA). There are two main types of MVPA, both with subcategories: Decoding Analysis and Response Similarity Analysis (RSA). Decoding Analysis is used to determine which stimulus out of a set elicited a specific neural response. It involves machine or statistical learning and can be a classification study (with discrete categories) or a regression study (with continuous variables, i.e., magnitude). RSA provides a way to examine and compare neural data across participants without the interference of response idiosyncrasies. The two subtypes of RSA are Representational Dissimilarity Matrix (RDM)comparison and Multidimensional Scaling (MDS)/Clustering. RDM can be used to determine how the brain distinguishes between different stimuli (information signature). MDS can be used to help us determine how different representations are stored in specific regions of the brain. Neuroimaging research is rapidly expanding and with it the types of statistical analysis able to be performed. This method of data analysis opens new doors for neuroimaging researchers as it expands the types and amount of data that is able to be collected and interpreted.
Weaverdyck, M. E., Lieberman, M. D., Parkinson, C. (2020). Tools of the trade multivoxel pattern analysis in fMRI: a practical introduction for social and affective neuroscientists. Social Cognitive and Affective Neuroscience, 15(4), 487-509. https://doi.org/10.1093/scan/nsaa057
10/14/22: Reproducible Brain-Wide Associations Require Large Sample Size
Author: Josie Zachman | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
This week in lab, undergraduate researchers Luke and Izzy presented “Reproducible brain-wide association studies require thousands of individuals” by Marek et al. (2022). They went over several main points of the article, which centered around how many brain-wide association studies (BWAS) are not reproducible due to low sample sizes. BWAS studies are used for examining the entire brain through a method of 3-D scans of the brain. There was a median of 25 subjects across BWAS studies, which is far too small to be replicable. This small number of participants inflates the effect size, thus partially falsifying data in underpowered studies. This was called a “reproducibility crisis in neuroimaging”. It was mentioned that there are exceptions to this need for incredibly high sample sizes, such as the studying of rare clinical disorders, where thousands of subjects are not available. This sparked discussion on the real possibility of actually getting thousands of subjects, despite the statistical importance. It was pointed out that many studies may first need to do a small study to see if there is any significance between brains that is worth examining further. We also mentioned the importance of case studies, and how these certain, individual effects can still be beneficial to the understanding of neuroanatomy, despite not having numbers in the thousands. This led to a discussion of the case of HM, in which the importance of consent was mentioned. This has real implications in our lab, since it is important for us to consider sample sizes in each and every project that we do. Power and effect size cannot be overlooked. Especially with our lab’s connections with MRI and fMRI data, we have to be careful that our data is not inflated. We need to be able to address our limitations and issues with replicability, though, working with children with ASD does not feasibly allow us to have sample sizes in the thousands. We also work to share data, with an emphasis on open science. This is one of the only ways that we can work towards larger sample sizes and peer reviews.
Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S.,...Dosenbach, N. U. F. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603, 654-660. https://doi.org/10.1038/s41586-022-04492-9
9/30/22: Broca’s Area Is Not a Natural Kind
Author: Isabella Talbert | Undergraduate Research Assistant in the CBrA Lab at the University of Alabama
During the CBrA lab meeting on September 30, 2022, we read and discussed “Broca’s Area Is Not a Natural Kind”, Evelina Fedorenko and Idan A. Blank (2020). The paper explores the heterogeneous construct of Broca’s Area, finding that Broca's Area has two computationally distinct regions: one that is involved with language processing (frontotemporal language-selective network), and another involved with executive functioning (domain-general frontoparietal MD network). Something that came up in our discussion was the high individual variability of Broca’s Area. Variability in the size, shape, and placement in individuals causes difficulty in studying the true function of Broca’s Area. By counteracting the offset of data due to positional differences of Broca’s area in the brain, research shows the possibility of two functions in this portion of the brain. Another idea that surfaced during the discussion was the difference in arithmetic comprehension versus language comprehension that leads to the creation of functionally different responses in Broca’s area. There are different functional responses to “sentence” versus “nonword’ stimuli. Ultimately, the domain-general region of Broca’s area had a greater functional response to tasks than the language-specific region. Their findings displayed that functional distinctions between the two areas inside Broca’s Area are present in task-based and non-task-based situations, including resting state and story comprehension which activate both regions. We also discussed the covariation of language and non-language regions. There is a strong effect size correlation between LIFG language region and other regions in the language network. Effects are seen across the brain when individuals display a strong response to language vs nonword stimuli. Interestingly, a similar effect is found in MD regions of the brain. Growing datasets have helped refine probabilistic activation atlases, specifying location with the most consistent function responses. These can also predict individual-to-individual variability in the locations of functional areas. As a result, cognitive deficits resulting from brain injuries can be accurately linked to either language processing or multiple-domain functional regions. Overall, the analysis of the paper sparked conversations about the variability of Broca’s area, and covariation of language and non-language regions, and raised questions about different functional responses. Ultimately, Broca’s area is both structurally and functionally heterogeneous, it is not a monolithic unit.
Fedorenko, E., & Blank, I. A. (2020). Broca's area is not a natural kind. Trends in Cognitive Sciences, 24(4), 270-284. https://doi.org/10.1016/j.tics.2020.01.001
9/16/22: Neuroanatomical Correlates of Autism Spectrum Disorder
Author: Emily Faircloth | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
Every two weeks, the CBrA lab meets to discuss an article pertaining to our research in the area of autism spectrum disorders. This week, we examined “Neuroanatomical correlates of autism spectrum disorders: A meta-analysis of structural magnetic resonance imaging (MRI) studies”, Del Casale et al (2022). This paper explores structural differences in the brains of autistic individuals as opposed to allistic individuals, finding that autistic brains showed a volumetric reduction of a large cluster involving the right-sided amygdala, parahippocampal gyrus, and putamen. The researchers conducted a meta-analysis using ALE, which is a coordinate-based method of examining areas of brain activation. Their findings showed structural differences that are compatible with clinical aspects of typical impairments associated with autism spectrum disorders. Something that came up often in our discussion was the study’s limitations. It was noted that the study drew data from outdated sources, that there were too many variables (unspecified medication, comorbidities, etc.), and that too few female participants were included. The latter point sparked a discussion about SMOTE, an oversampling technique used to create simulated data-points, and how it could be used to include more data about female participants. The group also noted the authors’ use of outdated language. Specifically, we criticized the fact that the authors referred to allistic individuals as “healthy”, as well as their note citing that the small number of female participants simply “reflects prevalence”. This resulted in a discussion about language, and how it can affect a project’s reception. Overall, the analysis of this paper raised concerns about careful research practices. As a researcher, you should be careful to ensure that your data is well-sourced and usable. You should also be mindful of the language you use, because derogatory or outdated language can be harmful to both the participants and the research itself. As a researcher, you have a responsibility to be diligent and mindful, which is something you can do by staying up-to-date and by ensuring careful research practices.
Del Casala, A., Ferracuti, S., Alcibiade, A., Simone, S., Modesti, M. N., & Pompili, M. (2022). Neuroanatomical correlates of autism spectrum disorders: a meta-analysis of structural magnetic resonance imaging (MRI) studies. Psychiatry Research: Neuroimaging, 325. https://doi.org/10.1016/j.pscychresns.2022.111516
9/2/22: Biomarkers and Resilience
Author: Daphne Rivera | Undergraduate Research Assistant in the CBrA Lab at The University of Alabama
For the CBrA lab meeting on September 2nd, we read and discussed “Neuroimaging Markers of Risk and Pathways to Resilience in Autism Spectrum Disorder” by Istvan Molnar-Szakacs, which was published in 2020. This paper conducts a literature review to determine the current understanding of neuroimaging and genetic biomarkers of autism. In addition, it discusses how these biomarkers relate to the concept of resilience. The paper delves into brain structure, function, and connectivity as well as expression of the Oxytocin gene. One of the things we discussed as a lab was differentiating the definition of biomarker as it is used in this paper in comparison to the traditional definition. In many instances, the term “biomarker” is used to indicate a measurable state that objectively indicates the presence of a certain state or condition in an individual. For example, individuals with Down Syndrome will have an extra copy of chromosome 21, which is a definitive indicator. The findings discussed in this paper are not really biomarkers in this sense, rather they don’t definitively indicate that an individual will or will not be diagnosed with autism. Instead, these findings indicate general trends that have been seen between individuals with and without autism which may be contributing to some differences. When reading a paper like this, it is important to understand what meanings is being attributed to a term in order to properly think about the results. Another thing we discussed is the utilization of the word resilience and how it relates to the autistic community. The paper defines resilience as adaptability in the face of hardship in order to experience a better-than-expected outcome. When discussing whether or not there was a way to define which areas of resilience are the most beneficial for autistic individuals, we realized that the definition of resilience provided by the article is quite vague, and that it does not necessarily provide the nuance needed to discuss the unique experiences of individuals in the autism community. Furthermore, when thinking about resilience and interventions, it is important to consider whether interventions are providing support for autistic individuals to be successful in the community, or if they are instead attempting to suppress or erase certain aspects of who a person is. Ultimately, this conversation emphasized the importance of considering the definitions we utilize when discussing research, since these impact the way we interpret and think about research findings.
Molnar-Szakacs, I., Kupis, L., & Uddin, L. Q. (2021). Neuroimaging markers of risk and pathways to resilience in autism spectrum disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(2), 200-210. https://doi.org/10.1016/j.bpsc.2020.06.017
