Donald Doherty's blog

Tag: Brain Science

  • Autism and the Brain: Recent Results from Brain Imaging Studies

    A recent review paper by Minshew and Keller looks at progress understanding autism and autism spectrum disorders using functional magnetic resonance imaging (fMRI) studies and functional connectivity fMRI (fc-fMRI).

    The paper is titled “The nature of brain dysfunction in autism: functional brain imaging studies” and was published April 2010 in Current Opinion in Neurology.

    Studies using fMRI correlate heightened brain-region activation with particular behaviors. The more recent technique of fc-fMRI enables the researcher to correlate the ability of one brain area to active another area with particular behaviors. It seems that some of the most exciting new insights into autism have been gained using fc-fMRI.

    Some of the findings discussed include:

    • Autism as a distributed cortical systems disorder resulting from the underdevelopment of connections between brain regions.
    • Structural imaging has shown accelerated brain growth beginning by 9–12 months of age coincident with the onset of symptoms and composed of increased total cerebral gray and white matter volumes.
    • Connections between the front and back (frontal-posterior regions) of the brain were commonly found to be less than normal in the autistic brain.
    • A study demonstrated the potential to increase connectivity in the autistic person. Ten weeks of reading intervention in poor readers resulted in improved reading and measurable changes in connectivity and white matter volume.
    • It’s easy to overestimate and exceed the language skills of a verbal person with autism. Although the autistic person may use the same words, their brains are wired differently. They show a greater reliance on visuospatial skills and the visual areas of the brain for solving both visual and verbal problems and reduced activity in the brain’s language areas.
    • When shown a movie, the brain in a person with autism perceived a different movie from other people with and without autism. The same person with autism saw a somewhat similar movie each time they viewed the same movie.

    In summary, the review paper by Minshew and Keller demonstrates clear progress in elucidating differences in structure and function of the typical brain from the brains of people with autism. Perhaps most exciting is that studies are appearing suggesting interventions based on our new knowledge.

  • New Optical and Genetic Tools to Help Distinguish Brain Cell Types

    The search for correlations between structure and function in the brain continues (see previous post “Brain Research Using Online Data Repositories: Brain Cell Shape and Function“). The line between structure and function has blurred as brain scientists distinguish brain cells based on gene expression and proteins in addition to shape, connectivity and electrical response properties.

    A new study from the laboratory of Clay Reid uses a technique called two-photon calcium imaging and new innovations using optical and genetic tools that enable them to identify the same cells in processed brain tissue that they recorded electrical activity from when the animal was alive.

    The paper “Broadly Tuned Response Properties of Diverse Inhibitory Neuron Subtypes in Mouse Visual Cortex” was published in the September 9, 2010 issue of Neuron.

    In this paper the authors describe using imaging in the live animal to study electrical signaling in brain cells. They injected a florescent marker into the area of the brain they were studying so that after they removed the brain they could locate and identify the same set of cells that they were studying in the live animal.

    They were able to distinguish between excitatory and inhibitory cells by using a genetically manipulated line of mice whose inhibitory cells all show up under the microscope when a specific label is used. They then focused on distinguishing subtypes of inhibitory cells using labels that they applied to the brain after its removal from the mouse. These labels find specific peptides or proteins and bind to them.

    While the mice were alive the investigators recorded changes in calcium concentration in these cells due to the presentations of light patterns to their eyes. Bars of light were presented in different orientations. Also gratings were presented.

    The team found that the subtypes of inhibitory cells they looked at had diverse characteristics but very similar visual response properties. In contrast, excitatory cells (pyramidal neurons) have similar characteristics but more diverse visual response properties.

    They conclude that “… excitatory neurons must receive sparser or more selective inputs to yield a preferred orientation that is independent of the local average.” However, another possibility is that both excitatory and inhibitory cells receive the same input but, the early arrival of an excitatory cells signal in the preferred orientation is expressed while the later signals arriving in the non-preferred orientations are suppressed by the inhibitory cells (see Wiggling Whiskers: Directional Tuning).

    Other related blog posts:

    Brain Research Using Online Data Repositories: Brain Cell Shape and Function

    Wiggling Whiskers: Directional Tuning

  • Brain Research Using Online Data Repositories: Brain Cell Shape and Function

    Today there continues to be no consensus on the classification of brain cells. Under the microscope there is a broad diversity of brain cell sizes and shapes. Beginning with the classical studies of Ramon y Cajal it’s been suggested that a relationship exists between brain cell shape and function.

    Brain cell classification based on shape has been largely subjective. Recording electrical properties of identified cells has helped to quantify shape and function. Nevertheless, classification schemes vary between the “lumpers” (few different categories) and the “spliters” (many different categories) and everything in between.

    A recent paper attempts to address brain cell classification based on shape by using cell data from the NeuroMorpho online data repository and clustering methods.

    The paper is titled “Investigating the Morphological Categories in the NeuroMorpho Database by Using Superparamagnetic Clustering” by Krissia Zawadzki and colleagues published in the March 15, 2010 issue of arXiv.

    NeuroMorpho is currently the largest online repository of the digital geometric representations of actual brain cells. This repository is an exciting start that could grow many orders of magnitude larger if every scientist is compelled to submit their morphology (cell shape) data at the same time they publish the results in a traditional journal.

    The analysis reported in this paper explored brain cell shape measurements from NeuroMorpho using a classification procedure of statistical physics known as Superparamagnetic Clustering (SPC). SPC is an unsupervised method for clustering. They also used Principal Component Analysis (PCA) and Latent Dirichlet Allocation (LDA).

    Their main objective was to compare the obtained clusters with the original classification in the repository and check the agreement with the original categories. My best assessment of their results leads me to conclude that the study was at best inconclusive.

    In my view, studies like this are important for exploring what may or may not provide useful results when analyzing data from large repositories like NeuroMorpho. Publishing negative results is as important as publishing positive results. Let’s hope scientists will publish more of their negative results and do so without necessarily writing them up to sound like positive results.