Alternative Therapies for Psychiatric Illness

Post by Leanna Kalinowski

What did we learn?

Research in 2021 took a step away from traditional therapies for psychiatric illness and towards transformative new techniques. First, we saw an increased benefit of incorporating technology into therapy. For example, the clinical benefits of personalized neuromodulation were uncovered when Grover and colleagues tested the effectiveness of non-invasive electrical brain stimulation on obsessive-compulsive behaviors. New technology, such as virtual reality, also showed promise this year when used as a delivery system for cognitive-behavioral therapy. Second, we saw an increased recognition of psychedelics as potential treatments for psychiatric disorders. For example, Shao and colleagues found that a single dose of psilocybin reduces depressive symptoms and triggers synaptic changes in the brain. Other psychedelics, such as mescaline, LSD, and MDMA, also show promise in treating disorders such as anxiety and PTSD.

What's next?

This research paves the way in better understanding how alternative techniques can be leveraged to (1) improve previously existing therapies or (2) develop new therapies for psychiatric disorders. Future research in 2022 will hopefully deepen our understanding of these techniques, and further establish their efficacy through clinical trials.

Multilingual Language Experience Affects Cognitive Function and Brain Connectivity

Post by Lina Teichmann

The takeaway

Multilingual children were shown to have enhanced executive function in comparison to monolingual children. In addition, functional connectivity in specific brain areas could be used to predict multilingual effects. This highlights that multilingualism has an effect on the brain and behaviour early on in development.

What's the science?

Being able to use several languages has been suggested to enhance executive function, as multilinguals must activate and suppress their known languages depending on the situation. This constant need to switch and juggle between languages may enhance attention and working memory. Recent studies, however, have failed to replicate these effects, and this failure may be partially due to high variability between multilinguals, age of foreign-language acquisition, level of proficiency, and the degree to which the language is used in daily life. This week in PNAS, Kwon and colleagues examined whether multilingualism in children affects executive function and brain connectivity.

How did they do it?

Using data from a large dataset of more than 1000 children (Adolescent Brain Cognitive Development, ABCD), the authors compared behavioural performances of monolingual and multilingual children with regard to working memory and attention tasks. In addition, whole-brain functional connectivity (correlated fluctuations in brain activity across different brain regions) was assessed by training a computer algorithm to distinguish the groups of children based on the connectome data only. The connectome was then further examined by comparing whether there are specific brain areas that are particularly engaged when the children perform a task and during a rest scan. To combine behaviour and neural data, the authors also used modeling approaches to predict behaviour from brain connectivity data alone.

What did they find?

The behavioural data showed that multilingual children performed better at working memory tasks. Using the functional connectivity data alone, the authors further found that a computer algorithm could differentiate between monolingual and multilingual children. There were stronger connections between prefrontal and occipital brain areas in multilingual children than monolingual children during rest, highlighting that multilingualism has an effect on brain areas usually associated with complex cognitive functions and visual processing. To quantify the relationship between brain and behaviour for memory function for the two groups of children, the authors used connectome-based predictive modeling. The results showed that the connectome of multilingual children engaging in a working-memory task can predict behavioural performance on working-memory tasks. In contrast, this was not possible for monolinguals.

What's the impact?

The advantages of multilingual language experience have been hotly debated over the last few decades. Kwon et al. demonstrated that there is indeed an effect of multilingualism on the developing brain and behaviour. This work provides important insight into how language can impact our brain development.

Access the original scientific publication here.

A Step Towards Personalized Classification in Epilepsy

Post by Andrew Vo

The takeaway

Treating patients with temporal lobe epilepsy is challenging due to the high degree of individual variability along the disease spectrum. Using machine learning and data-driven analysis of disease factors may allow for individualized patient diagnosis and care.

What's the science?

Despite sharing a common diagnostic label, patients with drug-resistant temporal lobe epilepsy (TLE) often display widespread and non-overlapping brain changes. Large individual variation along the disease spectrum has made it difficult to accurately characterize TLE patients and predict treatment responses using traditional “one-size-fits-all” group-level analyses. This week in Brain, Lee et al. applied machine learning to brain imaging measures to estimate the expression of TLE “disease factors” and tested how well these factors predicted clinical outcomes in TLE.

How did they do it?

The authors first obtained magnetic resonance imaging (MRI) of the brains of a large group of TLE patients. These non-invasive MRI measures described different structural and microstructural properties of the brain, including cortical thickness, myelin (white matter) changes, and gliosis (scarring in response to neuronal damage). They then applied machine learning to these data. Latent Dirichlet allocation is an unsupervised machine learning technique that estimates the co-expression of disease factors (i.e., patterns of disease-related brain changes) in each patient. Unlike more traditional approaches that aim to classify patients into a single subtype, the analysis technique used here allows multiple disease factors to be expressed to varying degrees in each patient. The authors determined the specificity of identified disease factors by estimating their expression in groups of healthy and other disease (i.e., frontal lobe epilepsy) control groups. Finally, they tested how well these disease factors could predict individual patient drug response and seizure outcomes after surgery, as well as the degree of cognitive dysfunction.

What did they find?

Four latent disease factors were identified. All factors were expressed to varying degrees in each TLE patient, not at all in healthy controls, and negligibly in frontal lobe epilepsy, thus highlighting the specificity of their findings. Factor 1 was characterized by microstructural changes, myelin loss, and atrophy largely in the hippocampus. Factor 2 was predominately marked by gliosis in paralimbic and hippocampal regions. Factor 3 was distinguished by bilateral neocortical thinning. Lastly, Factor 4 was largely marked with bilateral microstructural changes and minimal hippocampal changes.

The identified disease factors were then used to train classifier models, which predicted individual patient drug response and surgical outcome with 76% and 88% accuracy. Notably, classifiers trained on these disease factors out-performed untrained classifiers. Similarly, these disease factor-trained models could more accurately predict individual cognitive dysfunction (assessed in terms of verbal IQ, memory, and visuomotor learning) than baseline models.

What's the impact?

This study illustrated how using a machine learning approach can improve the characterization and prediction of clinical outcomes in TLE. Rather than classifying patients into a specific subtype, the strategy used here allows individuals to be represented along multiple dimensions that better capture their complex underlying pathology. Considering individual variability along the disease continuum will allow for personalized care and improved prognosis not just in TLE but other heterogeneous disease groups.

Access the original scientific publication here.