Effect of Early Life Events on Latitude Gradient for Multiple Sclerosis

Post by Lincoln Tracy

What's the science?

The prevalence, incidence, and mortality of multiple sclerosis (MS) has been reported to have distinct geographical and temporal patterns. Specifically, increasing latitude towards the poles is associated with increased prevalence. Scientists have considered latitude as a surrogate for environmental factors, such as UV exposure, given previous studies report a link between lower UV exposure and an increased risk of MS. An important limitation of previous studies has been the neglect of an individual’s long-term migration history, resulting in a failure to acknowledge the exposures accumulated over a lifetime. This week in Brain, Sabel and colleagues examined if and when lifetime population migration impacts the previously reported MS latitudinal gradient in New Zealand. They used data from a national MS prevalence study, conducted in tandem with the national population census.  

How did they do it?

The authors used data from the New Zealand national MS prevalence study, conducted on March 7, 2006. Specifically, they focused on a cohort of 1587 individuals with MS who were born in New Zealand and could provide a complete residential history from their conception to diagnosis. The geography of New Zealand was stratified into six broad latitudinal regions from the north to the south. First, they examined how lifetime population migration affects the latitude gradient for MS prevalence. To do this, they compared prevalence gradients between where people with MS were born, and where they were living in 2006. Second, they examined whether sex and disease course phenotype affected the latitude gradient. Third, they considered the effects of age and looked at what part of an individual’s life the effect of latitude on MS risk begins. Fourth, they then examined how different types of migration (i.e., diagnosed in 2006 at their 2006 location, born in a region other than where they lived in 2006 and then moved there, etc.) affected the MS risk gradients.   

What did they find?

First, the authors found a higher, but not statistically significant, gradient between the location of birth and MS prevalence when compared to a residential location in 2006. The stronger signal from the birthplace location is consistent with the latitudinal gradient becoming established prior to migration occurring. Second, they found the latitude gradient was driven by females with relapsing onset MS, rather than females with progressive MS or men with either kind of MS. Third, they found that the south to north latitude gradient was present at the time of birth. The gradient remained relatively stable until the age of 12 but then declined as individuals began to migrate. Fourth, they found that the gradient was being driven by individuals who either moved away from and then returned to their birth residence by 2006 or had never resided anywhere other than where they lived in 2006. This effect was particularly prominent for individuals in the southernmost region of New Zealand.

What's the impact?

This study demonstrates the gradient (and risk exposure) for MS is established early in life, potentially during gestation. The act of migration itself does not appear to influence the MS gradient in New Zealand. Rather, the effects of migration are more visible in subsequent years, where it dilutes the relationship between birthplace and latitude. These findings suggest that modifiable environmental factors to reduce the risk of MS (e.g., sunlight exposure, vitamin D deficiency), need to be addressed at the earliest possible stages of pregnancy and the neonatal period.  

Sabel et al. The latitude gradient for multiple sclerosis prevalence is established in the early lifecourse. Brain (2021). Access the original scientific publication here.

The Difference between an MRI Research Finding and a Psychiatric Diagnosis

Post by Anastasia Sares

“Why won’t my doctor…?”

Diagnosing a psychiatric illness is not always straightforward. Let’s take depression for example. The symptoms are not visible to the naked eye and can vary from patient to patient. On the other hand, there seems to be a wealth of brain imaging studies showing differences between people with and without depression. With all of these studies, it is tempting to think, “why won’t my doctor just give me an MRI scan to see if I have depression?”

To understand why, let’s take a simplified example: we have two groups of people, one group of males and another group of females (setting aside the complexities of gender identity for the moment). The only thing we know about these people is their height. Unless we have a very strange sample, we expect the groups to reflect the general population, with the female group having the smaller average height, and the male group having the larger average height. This, in our analogy, is similar to a research finding. Now, what if I pick a person at random and tell you that their height is 175 cm (5 feet 7 inches)? Could you reliably tell if they are from the male or female group? Not at all. This is similar to the challenge that arises when diagnosing a single person. To summarize, researchers can find subtle differences when they compare large groups of people with different psychological conditions, and this helps us to understand these conditions better. However, it can be difficult to classify any one person based on a brain scan.

Added to this is the expense of an MRI scan—these can cost hundreds to thousands of dollars an hour, and scheduling one will take time. Your doctor is constantly engaged in a cost-benefit analysis, trying to get you the most reliable diagnosis in the shortest time, and oftentimes an MRI may not be worth the cost. Why pay hundreds of dollars for a brain scan when a carefully validated questionnaire would also be effective?

So why are MRIs useful?

Firstly, there are several neurological conditions that can be diagnosed with an MRI, including strokes, tumors, and multiple sclerosis. The brain differences here are much easier to pick out, assuming sufficient training, and an MRI can help to determine definitively whether or not someone has the disease. In psychiatric conditions, MRI research has led to discovering much about the mechanisms behind different conditions. Let’s return to the previous example of depression. MRI has helped researchers to understand the involvement of certain brain regions in depression, like the frontal lobe and the amygdala, including how these regions differ in terms of their structure, function, and connectivity with other regions. This is also true for many other psychiatric conditions, such as obsessive-compulsive disorder, anxiety disorders or schizophrenia. In addition, MRI technology and analysis techniques are becoming more advanced every day. Researchers are now developing new MRI methods that may be able to visualize things at a higher resolution that we couldn’t see before. Techniques are also being developed that will help us to look at individual brain differences, and this can guide various personalized treatment approaches in psychiatry. Many also hope to employ artificial intelligence to identify more subtle abnormalities in scans and find people who might benefit from preventative treatments. If MRI costs were brought down somehow, the landscape of diagnosis might change dramatically as well.

What’s the bottom line?

What ultimately matters for a diagnosis is not always what your brain looks like, but rather what symptoms you’re having and how your daily functioning is affected. Although there are some diseases where MRI can be used to diagnose a patient, there are many cases where an MRI is complimentary or not necessarily needed. The usefulness of MRI in treatment will depend on whether looking at an MRI can help a clinician decide between various treatments (and whether it is worth the time and expense of a scan). MRI has provided immense value in understanding the causes and progression of many psychiatric diseases and this is crucial for the development of future treatments. As technology continues to advance, and if costs lower over time, MRI may become even more applicable to a wide variety of uses like diagnosis, guiding treatment, and monitoring recovery. 

References

Zhang, F. F., Peng, W., Sweeney, J. A., Jia, Z. Y., & Gong, Q. Y. (2018). Brain structure alterations in depression: Psychoradiological evidence. CNS neuroscience & therapeutics, 24(11), 994–1003. https://doi.org/10.1111/cns.12835

Lo, A., Chernoff, H., Zheng, T., & Lo, S. H. (2015). Why significant variables aren't automatically good predictors. Proceedings of the National Academy of Sciences of the United States of America, 112(45), 13892–13897. https://doi.org/10.1073/pnas.1518285112

Hunter SF. Overview and diagnosis of multiple sclerosis. Am J Manag Care. 2016 Jun;22(6 Suppl):s141-50. PMID: 27356023.

Foland-Ross, L. C., Sacchet, M. D., Prasad, G., Gilbert, B., Thompson, P. M., & Gotlib, I. H. (2015). Cortical thickness predicts the first onset of major depression in adolescence. International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience, 46, 125–131. https://doi.org/10.1016/j.ijdevneu.2015.07.007

Jollans, L., Boyle, R., Artiges, E., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J. L., Paus, T., Smolka, M. N., Walter, H., Schumann, G., Garavan, H., & Whelan, R. (2019). Quantifying performance of machine learning methods for neuroimaging data. NeuroImage, 199, 351–365. https://doi.org/10.1016/j.neuroimage.2019.05.082

Mechanisms Underlying Learning-Associated Neural Plasticity

Post by Lina Teichmann

What's the science?

Altering strategies or flexibly adapting to changes in any given environment is critical for survival. In the context of spatial navigation, learning leads to increased connectivity between the ventral hippocampus (vHPC) and the medial prefrontal cortex (mPFC). While this vHPC-mPFC connectivity enhances initial performance in the learned task, it makes it harder to flexibly adapt to new circumstances. This week in Nature, Park and colleagues examined neural mechanisms underlying adaptive learning. They tested mice on spatial learning tasks and investigated how novelty impacts vHPC - mPFC circuitry to allow for cognitive flexibility.

How did they do it?

Groups of mice freely explored a T-shaped maze in which they were rewarded for visits to either arm of the maze. Over the course of three days, mice simply chose one particular arm side to get the reward. Next, in a new ‘flexible choice’ task, the mice had to overcome their bias of choosing one arm over the other to receive a reward. To examine the effect of novelty on cognitive flexibility, a subgroup of mice was exposed to a new spatial environment or a new mouse before starting the flexible choice task. The mice which were exposed to novel environments learned to overcome their spatial bias and adapt to the new task more rapidly than mice who were not exposed to novelty before completing the task. This suggests that novelty had a positive effect on flexible learning. Recording neuronal activity from electrodes implanted into vHPC, dorsal HPC, and mPFC, the authors examined the neural firing reflecting learning-associated plasticity. In addition, they used optogenetics to stimulate vHPC terminals in the mPFC to directly examine the effect of novelty on vHPC-to-mPFC synaptic transmission. To test whether dopamine D1 receptors modulated learning through novelty, they also infused dopamine receptor agonists and antagonists.

What did they find?

Mice exposed to novel environments showed stronger theta rhythms, which are associated with learning. Theta rhythms reorganized the firing pattern of vHPC neurons in the novelty-exposed mice group, leading to a decrease in connectivity between vHPC and mPFC. This decreased connectivity means that adherence to the old task strategy is weakened, which more readily allows for adaptation to new task demands. In other words, to improve spatial learning in a new task, the vHPC – mPFC connectivity must first be reset, which is facilitated by novelty. Learning the new task strengthens the vHPC – mPFC connectivity once again and the mPFC encodes information associated with the old and the new task which allows for cognitive flexibility.

What's the impact?

This study demonstrates that novelty triggers a reset of vHPC - mPFC circuitry, enhancing new learning in mice. The findings elucidate the neural mechanisms involved in flexible adaptation to changing environments and open future avenues for examining how novelty affects learning in humans.

Park et al. Reset of hippocampal-prefrontal circuitry facilitates learning. Nature (2021). Access the original scientific publication here.