We have many models of human development, from personality and psychosocial ones to those based on neuroscientific and developmental research. Freud (1937), envisioning a scientific model for psychoanalysis and the mind, discussed our capacity for growth and development in Analysis Terminable and Interminable. Certain people are able to change to varying degrees, likening working with them to a sculptor working in “hard stone or soft clay.” Of those who are inflexible, he presciently writes:
“In another group of cases we are surprised by an attitude in our patients which can only be put down to a depletion of the plasticity, the capacity for change and further development, which we should ordinarily expect. We are, it is true, prepared to find in analysis a certain amount of psychic intertia… But with the patients I here have in mind, all the mental processes, relationships and distributions of force are unchangeable, fixed and rigid. One finds the same thing in very old people, in which case it is explained as being due to what is described as force of habit or an exhaustion of receptivity—a kind of psychical entropy. But we are dealing here with people who are still young. Our theoretical knowledge does not seem adequate to give a correct explanation of such types. Probably some temporal characteristics are concerned—some alterations of a rhythm of development in psychical life which we have not yet appreciated.” [italics added for emphasis]
Mind-Bending Developmental Research
Now, a population-level analysis in Nature Communications (Topological turning points across the human lifespan; Mousley et al., 2025) is making big waves. Analyzing diffusion MRIs (a form of magnetic resonance imaging looking at “tractography,” or the flow among brain regions and networks) from 4,156 people, ages 0 to 90, maps how structural brain networks evolve.
This work, complex and elegant, reveals four “turning points”—around ages 9, 32, 66, and 83—comprising five “epochs,” each with a distinct fingerprint. Rather than chasing single peaks, this multivariate, non‑linear model shows how many brain network features shift together at key ages—capturing complexity and nuance.
Five Epochs: Detail
Epoch 1 (0–9): From exuberance to organization
Major features:
- Decreasing global integration.
- Increasing local segregation.
- Small-worldness rises.
Developmental/clinical correlates:
- Synaptic pruning to shape neural networks, increased myelination of neurons (fatty sheaths which speed up nerve impulses), increased folding of the brain surface (allowing for more area in a small space).
- Cognitive gains: Language, attention, and early executive function.
- Mental health: Neurodevelopmental differences emerge, early risk for keeping things in (internalizing) or “acting out” (externalizing).
- Opportunities: Optimize early environment, secure attachment, support early social-emotional development.
Epoch 2 (9–32): The long adolescence of the connectome
Major features:
- Integration and small‑worldness increase.
- Global modularity decreases while local specialization strengthens.
- Small-worldness is the strongest predictor.
Developmental/clinical correlates:
- Brain changes shift with puberty for biological and psychosocial reasons.
- Mental health risk higher: Mood/anxiety, psychosis spectrum, and substance use vulnerabilities.
- Opportunities: resilience-building, education-to-work transitions, social support, sleep, and circadian alignment.
Epoch 3 (32–66): Midlife consolidation and rebalancing
Major features:
- Gradual decline in integration; steady increase in segregation/modularity.
- Local efficiency and clustering are strong predictors.
- Average connection strength rises while unused long-range connections fade.
Developmental/clinical correlates:
- Cognitive performance plateaus, personality becomes more stable, stressors include family and career, as well as increased medical risks, which can impact brain health (cardiovascular and metabolic).
- Prevention: physical activity, blood pressure and glucose control, hearing/vision care, cognitive load management, stress regulation.
Epoch 4 (66–83): Aging reorganization accelerates
Major features:
- Modularity becomes the most robust age-associated feature.
- Modularity strengthens; integration continues a modest decline.
Developmental/clinical correlates:
- Increasing medical problems; risk of dementia rises.
- Clinical focus: Dementia prevention, brain and overall health, remaining active and mentally challenged (“lifelong learning”).
Epoch 5 (83–90+): Diverging paths
Major features:
- Weaker coupling between age and overall topology.
- Subgraph centrality increases in select regions, potentially increasing redundancy in core areas.
Developmental/clinical correlates:
- Increasing importance of staying active and fit, avoiding frailty
- ty and preserving healthspan, greater variability from person-to-person.
- Care models: Person-centered goals, advanced care planning, community supports, attention to loneliness, and caregiver strain.
The illustration below captures the major findings, with age across the horizontal axis, and each epoch divided by vertical dashed lines. The wavy colorful lines show how major parameters shift, focusing on key factors, including: Overall level of brain integration, how modular different subsystems are, how interconnected subsystems are globally and locally, how “smart” the clustering is for speed and coherence of processing (“small world structure”), and how much each epoch has main brain areas which serve as key hubs (centrality and betweenness centrality, related to network resilience, as having too much function tied to one hub could lead to catastrophe if that hub is knocked out).
Takeaways and Caveats
The ages are averages; individuals follow unique paths shaped by biology, experience, and choices. At birth, the brain is hyper-connected, with abundant synapses that are pruned over time for greater global efficiency going into adolescence. Later, modularity increases with some rigidity. From adolescence to mid‑adulthood—peaking near 32 when the greatest shift occurs—the brain is receptive, and further developing as the mid-life phase approaches.
From about 32 until 66, directions stay similar, but white‑matter integrity declines with cognition as shown in prior work and implied by findings in this research, while modularity rises, favoring specialization over broad adaptability. After 83, changes stabilize: Modularity settles, and subgraph centrality adds redundancy in select regions, supporting steadiness and emotional sturdiness at the cost of flexibility. Staying healthy and mentally challenged matters as networks adopt aging patterns that can tilt positive or negative.
Findings align with prior gray‑ and white‑matter work. Limits include the study’s cross‑sectional design; its strengths are in its nuanced, multifactorial analytics.
Future longitudinal studies might test which factors shift trajectories, employing emerging artificial intelligence-enabled analytic tools and richer data sets, as well as additional measures of brain function, enabling precision prevention and intervention and timing support for learning, resilience, and treatment to protect cognition and mental health across the lifespan.