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Innovation: MRI-based technique allows researchers to non-invasively follow stem cells in vivo.

Neural stem cells are born deep in an area of the brain called the subventricular zone. As time goes on, the cells, also called neuroblasts, make their way to other areas of the brain where they mature into functioning neurons. The brain’s ability to regenerate its cells is of great interest to scientists.

The MRI could be used to develop treatments for brain injury caused by trauma, stroke, Parkinson’s disease, and other neurological disorders.

Eric Ahrens, associate professor of biological sciences at Carnegie Mellon University.

To hoard or not to hoard…

But first, let’s define the term hoarding… According to An et. al (2009), compulsive hoarding is both the acquisition of and the inability to discard a large number of possessions that appear to be useless and have no value. As some of you may expect, compulsive hoarding is present in many cases of OCD, and has been usually been studied in that clinical context. 

Throughout the years, neuroimaging studies have linked hoarding symptoms in OCD patients with multiple brain areas. Mataix-Cols et. al (2004) found distinct neural correlates for the different symptoms present in OCD patients such as washing, checking and hoarding. Importantly, the study found that hoarding behaviors were most significantly correlated with right orbitofrontal cortex and left precentral gyrus activation.  Other brain areas implicated in hoarding behaviors in OCD patients include the right sensorimotor cortex and the fusiform gyrus. Additionally, lesions studies of human ventromedial prefrontal cortex have resulted in the emergence of hoarding behaviors that were not present before, which combined with other findings in patients with hoarding symptoms, has led to the hypothesis of ventromedial prefrontal cortex involvement in compulsive hoarding.

In order to determine the neural correlates of hoarding symptoms in OCD patients, An and colleagues recruited three subject groups: a group w/ OCD and strong hoarding symptoms, a group w/ OCD but no significant hoarding symptoms and a group of healthy controls. The image above is part of the results of this study, and shows the brain regions significantly more activated in hoarders than in non-hoarders and controls (shown in red in (a) and (b), and in healthy controls more than in hoarders and non-hoarders (shown in blue in (b) and (c) during hoarding symptom provocation. The box plots depict the percent change in blood oxygen level dependent (BOLD) responses for each group.  

All three groups showed increased activation in widespread areas of the brain such as:

• Ventral, paralimbic, and dorsal prefrontal brain regions (including bilateral visual regions)
• Ventrolateral, dorsolateral, and dorsomedial prefrontal regions
• Cerebellum
• Anterior insula
• Temporal cortex

However, some areas were only activated in specific groups. For example, only hoarders activated large clusters in the frontal pole (including a large bilateral cluster in the anterior vmPFC), extending ventrally to the anterior part of the orbitofrontal cortex and dorsally to the medial frontal gyrus. Specifically, this vmPFC cluster is spatially situated anterior to areas implicated in the decision-making circuit. The non-hoarding OCD group significantly activated the caudate putamen while the controls showed activation in the striatum, left thalamus and the vmPFC (including the orbitofrontal cortex and the anterior cingulate cortex).

Note: The study also emphasized on the role of anxiety in compulsive hoarding symptoms and related brain activation. They suggest that OCD patients with compulsive hoarding symptoms have higher anxiety levels (provoked by the experimental simulation task of “choosing” which objects to discard) that correlate with greater activation in the vmPFC and other temporal lobe areas like the uncus, parahippocampal gyrus, hippocampus, amygdala, and more. Mataix-Cols et. al have also found an effect of anxiety on brain activation during a similar task.

Sources:

An, SK, et. al. 2009. To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder. Molecular Psychiatry. 14 (3): 318-31. doi:10.1038/sj.mp.4002129

Mataix-Cols, D. et al. 2004. Distinct neural correlates of washing, checking, and hoarding symptom dimensions in obsessive-compulsive disorder. Arch Gen Psychiatry. 61 (6): 564-576. 

Dyslexia

When most people think of dyslexia, they think reading disability. Early explanations of dyslexia attributed the disorder to the defects in the visual system. With time, subsequent research implicated the language system and related brain structures.Currently, dyslexia is thought to reflect a deficient processing of the most basic linguistic units (phonemes), which are the building blocks of spoken language. 

Because words are made up of different combinations of phonemes, the brain must break a word down into its phonetic units before the word can be identified, understood, gain meaning or stored in memory. In spoken language, this process is thought to occur automatically. According to Noam Chomsky and Steve Pinker, language is instinctive and pretermined, which means that humans are prone towards learning and using language (see Universal Grammar Theory). In this phonological model of language, the speaker automatically assembles phonemes into words with meaning via the human speech apparatus: larynx, palate, tongue and lips. Thus, the merging of phonemes results in a unit of sound that has meaning. 

Although speaking is thought to occur naturally and automatically, reading is not.In contrast to speaking, reading requires constructing invented sounds and must occur at conscious level. So what do you do when you read? Reading is basically transforming visual information into sound information by “recoding” graphemes (written letters) into phonemes. Thus, in order for a reader to understand the written words in a page, he or she must first have a conscious awareness that the letters on the page represent the sounds of spoken words as well as the structure of these words and their sequence in the page, all of which confer meaning. According to the phonological deficit hypothesis, when a child is dyslexic, a deficit at the phonological level of the language system hinders the child’s ability to separate the written words into its basic phonological components.

With the development of functional magnetic resonance imaging (fMRI), scientists have been able to delve into the neurobiology of reading. Areas found to be relevant to reading and related functions include:

• Extrastriate cortex (in the occipital lobe): involved in the identification of letters. 
• Inferior frontal gyrus: involved in phonological processing. This structure’s role in phonological processing is very exciting because gender differences have been found: men primarily show unilateral activation whereas women show bilateral activation. 
• Middle/superior temporal gyri: implicated in meaning. 

Neuroimaging in dyslexics have also found differences in other brain structures including but not limited to:

• Reduced size in temporo-parietal language regions
• Increased size in corpus callosum (splenium and isthmus)
• Abnormal maturation of cortical brain areas
• Broca’s area (left inferior frontal cortex)
• Weaker connectivity between anterior and posterior language regions

Additionally, newer studies are suggesting possible involvement of cerebellar regions in dyslexia.

So what do you guys think? Is dyslexia really a reading disability, or can it be considered a learning disorder? 

References:

Heim, S & Andreas Keil. 2004.Large-scale neural correlates of developmental dyslexia.European Child and Adolescent Psychiatry. 12: 125-140. DOI 10.1007/s00787-004-0361-7

Shaywitz, Sally E. 1996. Dyslexia. Scientific American.

Amygdala volume correlates with the size and complexity of social networks in humans

Evolutionarily, one of the most important social challenges is to be able to distinguish between friend and foe, which can aid in survival. The social brain hypothesis, which suggests that living in larger and complex social groups selected for larger brain regions capable of performing relevant computations. One of these brain regions is the amygdala, a critical structure for learning, memory and emotion that has been implicated in mood disorders, social behavior, and interpersonal relationships (i.e. mother-infant interactions). Because of its central functional role and anatomical position, the authors proposed that amygdala volume should be associated with size of social network (size is typically considered an indicator of processing capacity. Moreover, neuroimaging studies done in nonhuman primates have supported this association between an enlarged amygdala and larger social groups.Thus, there is a notion that a larger amygdala volume enables increased processing of social demands that form part of life in a social group or hierarchy.

In a 2011 study, Bickart et al. examined whether amygdala volume varies as a function of individual variation in the size and complexity of social groups within humans. The group examined the social networks, the number of people that the individual maintains or “regular contacts” (also an indication of overall network size), in approximately 58 healthy adults (healthy= absence of DSM-IV diagnoses). The group also employed another social scale to measure the number of different groups that the contacts belonged to, reflecting network complexity. Furthermore, the performed quantitative morphometric analyses of MRI data. According to Bickart, linear regression analyses revealed that subjects with larger and more complex social networks had larger amygdala volume (even when controlling for variables such as age) with no lateralization of effect. The group found no significant differences in other non-social brain structures like the hippocampus and other subcortical structures. 

Sources:

Bickart, et al. (2011). Amygdala volume and social network size in humans. Nature Neuroscience. 14:163-164. doi:10.1038/nn.2724

Image: http://www.nature.com.ezproxy.med.nyu.edu/neuro/journal/v14/n2/fig_tab/nn.2724_F1.html