Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Causes of Social Behavior II: Cognitive Processes01:15

Causes of Social Behavior II: Cognitive Processes

228
Cognitive processes affect social behavior by guiding how individuals perceive, interpret, and respond to social stimuli. These mental processes enable individuals to assess others' behaviors, attribute causes to their actions, and form expectations based on past experiences.Causes of Behavior and Social JudgmentsIndividuals determine the causes of others' behaviors by distinguishing between personal traits and external circumstances. For example, if a friend frequently arrives late, an...
228
Intellectual Disability01:29

Intellectual Disability

816
Intellectual disability (ID) is a neurodevelopmental condition characterized by deficits in intellectual and adaptive functioning that manifest during the developmental period. This condition encompasses challenges in reasoning, memory, problem-solving, and learning, accompanied by impairments in everyday life skills, such as communication, self-care, and social interactions. Intellectual disability affects approximately 1% of the population in the United States, impacting an estimated 5...
816
Information Processing Approach01:30

Information Processing Approach

620
The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
620
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

274
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
274
Biological Influences on Intelligence01:30

Biological Influences on Intelligence

609
Intelligence is often thought to be linked to brain size, but the relationship is more complex than that. While brain size does correlate modestly with some abilities, like verbal skills, the connection is weaker for others, such as spatial reasoning. Other factors, like brain structure, also play crucial roles. For instance, despite Einstein's smaller-than-average brain, his parietal cortex, which is involved in spatial reasoning, was 15% wider, suggesting that neural density might matter...
609
Environmental Influences on Intelligence01:29

Environmental Influences on Intelligence

984
Despite the strong genetic influence on traits like intelligence, environmental factors significantly shape outcomes. For example, while over 90% of height variation is due to genetic differences, environmental factors such as nutrition also have a notable impact. Similarly, for intelligence, changes in a child's surroundings can significantly alter their IQ. Research shows that enriched environments boost children's academic success and help them develop key cognitive skills. Children...
984

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prognostic Value of Blood-Based P-Tau217 Levels for Progression to Cognitive Impairment.

JAMA·2026
Same author

Depression and hippocampal subfield volume in older adults.

Translational psychiatry·2026
Same author

Blood biomarkers predict conversion from cognitively stable to mild cognitive impairment or Alzheimer's disease in Down syndrome at 16-month follow-up in ABC-DS.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Programmes that Integrate Parenting Support and Financial Well-Being Support: A Systematic Scoping Review.

Journal of prevention (2022)·2026
Same author

Alcohol use and <i>APOE ε</i>4 interaction with cognitive domains among American adults from diverse racial/ethnic groups: A HABS-HD study.

Alzheimer's & dementia. Behavior & socioeconomics of aging·2026
Same author

Tackling the complexity of cancer with generative models.

Cell·2026

Related Experiment Video

Updated: Feb 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K

Predicting low premorbid cognitive ability with social determinants: A machine learning approach.

Lubnaa Badriyyah Abdullah1, Ibshar Khandakar2, Ashley Douglas3

  • 1UNT Health Fort Worth, Department of Family Medicine, Institute of Translational Research, Fort Worth, TX 76107, United States.

JAR Life
|February 18, 2026
PubMed
Summary

Individuals with low premorbid intellectual ability (pIQ) are more vulnerable to social determinants of health. This vulnerability, linked to neighborhood deprivation and reduced support, may accelerate cognitive decline pathways.

Keywords:
Alzheimer’s diseaseArea deprivation indexInflammationIntellectual disabilityMachine learningPremorbid intellectual abilitySocial determinants of health (SDoH)

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K

Related Experiment Videos

Last Updated: Feb 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K

Area of Science:

  • Exposome research
  • Neuroscience
  • Public Health

Background:

  • Social determinants of health (SDoH) and the exposome influence biological processes and Alzheimer's disease (AD) risk.
  • Individuals with low premorbid intellectual ability (pIQ ≤70) may exhibit heightened vulnerability to SDoH due to reduced cognitive reserve.
  • The interplay between low pIQ and SDoH in AD risk remains understudied.

Purpose of the Study:

  • To investigate the association between SDoH and low pIQ.
  • To identify key SDoH factors predicting low pIQ.
  • To understand the psychosocial-cognitive phenotype associated with low pIQ and SDoH.

Main Methods:

  • Analysis of data from the Health and Aging Brain Study-Health Disparities (n=2691).
  • Classification of participants into low pIQ (≤70) or average pIQ (90-100) groups using word reading scores.
  • Application of an XGBoost machine learning model to predict pIQ grouping based on education, income, Area Deprivation Index (ADI), social support, stress, health status, and worry.

Main Results:

  • The XGBoost model achieved an Area Under the Curve (AUC) of 0.72 [0.64, 0.81].
  • Key predictors of low pIQ included worry, ADI, income, high school completion, and tangible support.
  • Low pIQ was significantly associated with greater neighborhood deprivation, lower income, and fewer support resources.

Conclusions:

  • Low pIQ combined with SDoH factors identifies a vulnerable psychosocial-cognitive phenotype.
  • This phenotype may accelerate cognitive decline, potentially via inflammatory mechanisms.
  • Understanding these pathways is crucial for developing targeted interventions for at-risk populations.