Archive for science

The Unique Processing in Autistic Brains Explained

Posted in Clinical topics, Neuroscience with tags , , , on September 2, 2025 by nvm.m

The current image has no alternative text. The file name is: create-a-featured-image-illustrating-the-intricate-architecture-of-the.png

The Autistic Architecture

Autism Spectrum Disorder (ASD) is a complex condition with diverse symptoms, affecting how individuals interact with the world and process information. 

Heuristics of Autism

Normally, the brain is constantly making predictions about the world and adjusting those predictions based on new information it receives. This process is called “Bayesian inference.” In simple terms, the brain uses its “prior beliefs” (what it already expects) and new “sensory data” (what it sees, hears, touches, etc.) to figure out what’s happening.

A key concept in understanding autism from this perspective is “divisive normalization.” It is like a surge protector that balances incoming information. In a typical brain, this helps to prevent overstimulation and focuses attention. In autism, this “volume knob” might be turned down or function differently, leading to:

  • Hyper- or Hypo-reactivity to sensory input: Some autistic individuals might be overwhelmed by bright lights or loud sounds (hyper-reactivity), while others might not notice things most people would (hypo-reactivity).
  • Atypical integration of multisensory information: It’s like trying to listen to two different conversations at once – the brain struggles to combine information from different senses (sight, sound, touch) into a coherent picture.
  • Increased sensitivity to sensory noise: Imagine trying to hear someone speak in a very noisy room. For autistic individuals, the brain might have a harder time filtering out the “noise” and focusing on the important sensory “signal.”

Processing Strategies

The structural differences in the autistic brain,  contribute to the widely described characteristics of autism:

  1. Perceptual Processing: As mentioned above, this relates to how sensory information is handled. The “divisive normalization” issue means the brain might interpret sensory input differently, leading to unique ways of perceiving the world. For example, some autistic individuals might excel at tasks requiring fine detail, but struggle with integrating broader information.
  2. Social Communication and Interaction Deficits: This is a core symptom. Typical brain communication is like a symphony orchestra, with different sections playing together in harmony. In autism, some parts of the orchestra might not be playing in sync, or certain instruments might be too loud or too quiet. This “atypical functional connectivity” (how different brain areas communicate) affects social understanding.
    • “Underconnectivity” in large-scale networks: It’s like having weak internet signals between important brain regions that are supposed to work together for social interactions.
    • “Theory of Mind” (ToM) deficits: This is the ability to understand that others have their own thoughts, feelings, and perspectives. If the brain’s “social networks” are not communicating effectively, it can be harder to “read” other people.
  3. Restricted and Repetitive Behaviors (RRBs) and Interests: These behaviors, like repetitive movements or intense focus on specific topics, are also linked to brain differences. It’s like the brain getting “stuck” in certain patterns or having trouble shifting gears. This can involve issues with:
    • Inhibitory control: The brain’s ability to stop or regulate actions.
    • Frontostriatal circuits: Brain pathways involved in habits and routines.
  4. Emotion Regulation and Self-Awareness: Autistic individuals often find it challenging to recognize, express, and understand their own emotions, as well as those of others. This can make navigating social situations difficult.
  5. Cognitive Flexibility and Executive Function: This refers to the brain’s ability to adapt to new situations, switch tasks, and plan. In autism, there can be “cognitive inflexibility,” meaning difficulty with change and a preference for routines. This can be influenced by how the brain grows and develops.

Brain Development and Connectivity

Unusual brain growth trajectories in autism can disrupt how different brain regions connect and communicate. Imagine roads being built in unusual ways, leading to detours or dead ends. This “fall-off” in connectivity can lead to more localized processing rather than seamless communication across larger brain networks.

Tools for Understanding the Autistic Brain

Researchers are using advanced tools like “computational models” and “machine learning” to better understand autism. These are like sophisticated simulations that help scientists:

  • Predictive Models: Help with diagnosis and understanding how autism changes over time.
  • Identify Subgroups: Find distinct groups of autistic individuals based on their brain patterns and symptoms, which could lead to more personalized treatments.
  • Map Brain-Behavior Connections: Figure out which brain differences are linked to specific behaviors.

In essence, autism can be understood as a difference in how the brain computes and processes information, often due to imbalances in its internal “volume controls” and communication networks. This understanding, informed by genetics and brain development, opens doors for more precise diagnoses and tailored interventions for each individual on the autism spectrum.

Neuroeconomics

Posted in Neuroscience, social behavior with tags , , on January 12, 2013 by nvm.m

While common opinion would say that any individual with a cognitive deficit (including the affective component) would be at a disadvantage at most complex (socially relevant) tasks, this is not necessarily true.
My personal experience is full of examples (like the fact that concrete thought enables you well for medical school). However, it is always nice to bump into some published evidence, like the advantage of brain-damaged investors.
On a wider perspective, predicting a system’s behavior approaching it as a black box can only guarantee an adequate predictive model if the number of observations is considered infinite. Otherwise, for practical purposes, some cases justify looking into its inner structure and workings, as it appears to be in the case of  neuroeconomics.