Neurodivergence

Definition & Core Concept

Neurodivergence refers to natural variations in human brain function and cognition that differ from what is considered neurotypical.

The term stems from the neurodiversity movement, coined in the late 1990s by sociologist Judy Singer, who framed neurological differences as a form of human diversity, not pathology.

People whose cognitive functioning diverges significantly from social norms are described as neurodivergent (e.g., autistic, ADHD, dyslexic, dyspraxic, Tourette’s, bipolar, etc.).

Major Theoretical Models

Medical / Deficit Model

Views neurodivergent conditions as disorders needing diagnosis and treatment.

Focuses on symptom management, impairment, and “normalisation.”

Still dominant in clinical and educational systems, but increasingly challenged.

Social Model of Neurodiversity

Argues that “disability” arises largely from mismatch between diverse brains and rigid social environments.

Emphasises acceptance, accommodation, and systemic change rather than “fixing” individuals.

Inspired by disability rights and the social model of disability.

Biopsychosocial Model

Integrates biological, psychological, and social factors.

Recognises both innate neurological differences and social contexts (stigma, accessibility, stress) as shaping outcomes.

Ecological / Contextual Models (2020s–present)

View neurodivergence as a dynamic interaction between brain, environment, and culture.

Neuroscience, genetics, and epigenetics show high intra-group variation — supporting the idea of a spectrum rather than discrete categories.

Prominent in current cognitive science and inclusive education research.

Neuroscientific & Psychological Perspectives

Brain connectivity differences (especially in autism, ADHD, dyslexia) are viewed as part of normal human variability in neural networks.

Neurodivergent strengths: pattern recognition, creativity, hyperfocus, divergent thinking, empathy, spatial reasoning, etc.

Executive function and sensory processing differences explain many everyday challenges.

Current research focuses on neuroplasticity and contextual support rather than “correction.”

Social & Cultural Dimensions

Neurodiversity Paradigm: Advocates equality and acceptance, arguing neurotypes should be seen as equally valid ways of being.

Identity-first language (“autistic person” vs “person with autism”) is preferred by many in neurodivergent communities.

Intersectionality: Race, gender, and class influence diagnosis rates, stigma, and support.

Growing attention to late diagnosis and masking — especially among women, LGBTQ+, and people of colour.

Current Debates & Challenges

Medicalisation vs empowerment: balancing need for support with avoiding pathologisation.

Diagnostic categories: whether labels (e.g., ASD, ADHD) are helpful or limiting; calls for spectrum-based or dimensional models.

Inclusion vs assimilation: true inclusion means changing environments, not forcing conformity.

Neurodiversity in workplaces & education: emerging policies on accessibility, sensory-friendly design, and flexible communication norms.

Implications

Neurodivergence reframes mental and cognitive variation as part of the natural biodiversity of the mind.

The focus shifts from “deficit correction” to designing systems (schools, workplaces, technologies) that accommodate different ways of thinking.

Neurodiversity oriented research and advocacy are reshaping psychiatry, education, and employment inclusion worldwide.

Summary of recent neuroscience findings about neurodivergence (autism, ADHD, and related neurodevelopmental differences).

Key Recent Findings

Autism is not one uniform condition

A large international study (~45,000 autistic individuals) found that those diagnosed in early childhood (before ~6 years) have different genetic profiles from those diagnosed later (adolescence).

Early-diagnosed cases more often show earlier behavioural difficulties; later-diagnosed often show symptoms emerging or being recognised during adolescence and have more overlap with mental health conditions like depression, PTSD, ADHD.

Distinct but overlapping neural signatures in autism vs ADHD

A major study (NIH + King’s College London, children/adolescents 6-19 years) compared resting-state functional connectivity in ASD vs ADHD.

Findings:

• ASD is associated with reduced connectivity between the thalamus, putamen, salience/ventral attention network, and frontoparietal networks.

• ADHD shows stronger connectivity in some of those same circuits.

• Both show hyper-connectivity between Default Mode Network (DMN) and Dorsal Attention Network relative to neurotypical peers.

Cortical anatomy differences, and how co-occurrence and sex/age matter

Using big brain imaging datasets and normative modelling, researchers characterised how autism, ADHD, and their co-occurrence compare to typical brain development.

Autism: more localised increases in cortical thickness & volume in regions like superior temporal cortex.

ADHD: more global increases in cortical thickness, but decreased cortical volume and surface area across much of the brain.

When both autism & ADHD are present (“ASD+ADHD”): distinct pattern combining widespread increased cortical thickness + region-specific decreases in surface area.

Sex and age effects: sex modulates autism’s neuroanatomy; age-by-diagnosis interaction seen for ADHD (i.e., how structural features vary over development).

Epigenetics, developmental trajectories, and early brain development

A map of DNA methylation in human brains (from ~6 weeks post-conception into old age) shows that genes linked to autism & schizophrenia undergo dynamic methylation changes early in life (before birth).

Frameworks such as “normative modelling + deep generative embedding” are being developed to chart how individual brain connectivity diverges from typical development over time (i.e. “connectivity brain-age” vs chronological age) and to map regional deviations. Example: a model called BRIDGE.

Functional modulation rather than static connectivity differences

Some recent work suggests that what is different in autism may not (only) be resting static connectivity, but how neural networks respond or modulate under certain neuromodulatory or neurotransmitter influences (e.g. GABA, serotonin, mu-opioids).

Genetic overlap and shared risk across neurodivergent conditions

Dyslexia & ADHD: researchers found ~49 genomic regions associated with both dyslexia and ADHD, many new. These map to genes involved in synaptic signalling, neuronal development.

Also, autism & ADHD both show enriched involvement of structural anatomy (fronto-temporal, limbic, occipital) and some shared genomic underpinnings, though there are also distinct genes.

Inflammation and broader health outcomes connected to neurodivergent traits

Children with neurodivergent traits (autism or ADHD) are more likely to develop chronic disabling fatigue by age 18. Some of this effect is statistically mediated via elevated inflammation markers (like IL-6) in earlier childhood.

Molecular / biochemical pathways implicated

Dysregulation of prostaglandins and cyclooxygenase (COX) enzymes are being studied in autism; COX / prostaglandin signalling may influence dendritic branching/arborisation and cerebellar function — those are key to neural connectivity and integration.

Implications & Emerging Trends

Heterogeneity is real and important: Autism (and neurodivergence more generally) is not one thing. Variation by age of onset, co-diagnosis (ASD + ADHD), sex, genetic risk, and developmental trajectory all shape different “biotypes” or subtypes. This has implications for diagnosis, support, interventions.

Need for longitudinal & developmental models: Early brain development (even prenatal) matters; trajectories (how things change over time) are as informative or more so than static snapshots.

Connectivity & network dynamics are central: instead of focusing only on single brain regions, recent work looks more at how regions communicate (functional connectivity, network strength/weakness, modulation under neurotransmitters).

Multi-modal measures are useful: combining imaging, genetics, epigenetics, molecular biology, and behaviour yields a richer, more precise understanding than any one domain.

Biological markers & personalised profiles: Models like BRIDGE (connectivity brain-age, region-wise neurodivergence maps), and the observation that genetic profiles differ depending on when autism is diagnosed, push toward more tailored support or interventions rather than one sise fits all.

Role of immune system & inflammation: Growing evidence that inflammatory processes (prenatal, early childhood) are linked to neurodivergent traits, fatigue, perhaps symptom severity or co-occurring health issues.