A Mobile Messaging Intervention Can Improve Depression Symptoms in Older Adults

Post by Lila Metko

The takeaway

Adequate mental health care for older adults is a growing concern in low and middle-income countries. A new mobile messaging intervention, developed to combat this issue, was able to help older adults with depressive symptoms in Brazil by improving their condition to a less depressed state.

What’s the science?

Given that 69% of the world's older population live in low and middle-income countries, and that the healthcare workers in these countries are already overstretched with a heavy workload, a virtual yet efficacious option for mental health treatment is needed. This week in Nature Medicine, Scazufca and colleagues assess the effectiveness of a mobile messaging intervention in older adults with depressive symptoms, that uses two techniques called psychoeducation and behavioral activation. Psychoeducation is a specific way of educating patients about their condition, which can have a therapeutic value to them. Behavioral activation is the encouragement of the individual by their mental health provider to engage in a constructive activity like going for a walk in nature or calling a good friend. 

How did they do it?

The researchers recruited 603 participants who were over the age of 60, experienced depressive symptoms, and had access to WhatsApp mobile messaging. These participants were recruited from underprivileged areas in a major metropolitan region of Sao Paulo, Brazil. Participants were randomly separated into two groups, an intervention arm, and a control arm. The intervention arm participants received a self-help mobile-based intervention developed based on the principles of psychoeducation and behavioral activation. Forty-eight WhatsApp messages in either video or audio format (depending on the message) were sent to the participants so that they received a message twice a day, four days a week for six weeks. The audio messages were 3 minutes long, and contained fictional characters who discussed symptoms of depression and potential interventions, mentioning fictional situations of interventions that had been beneficial in the past. The visual messages were summary diagrams designed to reinforce the audio message content. The control group received only one six-minute message that briefly discussed symptoms of depression and how to manage them.

Participants’ level of depressive symptomology was measured at baseline, three months after the intervention, and five months after the intervention. The two measures assessed related to symptomology were: improvement in depressive symptomology and reduction in depressive symptomology. Improvement in depressive symptomology constituted having a score on the depressive symptom questionnaire below a specific value and reduction in depressive symptomology constituted having a reduction in their depressive symptomology questionnaire score by at least 50%.   

What did they find?

Participants in the intervention arm had improvement and reduction in depressive symptomology at three months but not at five months. A complier average causal effect (CACE) analysis indicated that, for the three-month time period, as the number of messages opened by the participants increased (compliance), so did the effect of the intervention.

What’s the impact?  

Many low and middle-income countries such as Brazil have a treatment gap for mental health. This is especially concerning due to the growing population of older adults, and the need for mental health care. Roughly 30% of Brazilian older adults in low-resource settings experienced symptoms that could indicate clinical depression. This research indicates that self-help interventions for depression are feasible and efficacious in low and middle-income populations.

Access the original scientific publication here.

Risk Factors Affecting Brain Regions Vulnerable to Aging and Disease

Post by Meagan Marks

The takeaway

The “LIFO” network is a collection of high-functioning brain regions most susceptible to decline by aging and disease. Particular genes and modifiable risk factors such as diabetes, pollution, and alcohol intake are shown to be linked to the vulnerability of these regions.

What's the science?

Regions of the brain associated with high-process functions such as memory, attention, and execution tend to degenerate earlier and faster than the rest of the brain. These regions are also the last to develop during adolescence, coining them the “last in, first out” (LIFO) network. It is known that these LIFO regions are especially vulnerable to diseases like Alzheimer’s, Parkinson’s, and Schizophrenia, however, the genetic and modifiable risk factors that influence the sensitivity of these regions remain unknown. This week in Nature Communications, Manuello and colleagues aimed to identify these factors via statistical analysis to understand further how the LIFO network is regulated and to determine potential behaviors that may shield or exacerbate its decline.

How did they do it?

To determine the specific genes and modifiable risk factors linked to the LIFO network, the authors ran a series of statistical tests using data from the UK Biobank, a large-scale biomedical database that holds genetic, lifestyle, and health information of thousands of participants. To begin, the authors first calculated the grey matter volume of the LIFO regions of nearly 40,000 participants using brain scans obtained from the biobank. This allowed the authors to determine how much degeneration had taken place (less volume = more degeneration). Next, using computational analysis, the authors were able to sift through the genomes of participants to identify which genes were significantly associated with the grey matter volume of the LIFO network and which variants, or versions, of these genes were significantly associated with a lower volume. Finally, the authors looked at the association between the LIFO network and 15 modifiable risk factor categories that were previously linked to dementia. The authors were able to use the health and lifestyle data of biobank participants to do so, statistically determining which of these factors were significantly associated with LIFO network grey matter volume.

What did they find?

After genomic analysis, the authors identified seven gene clusters, or groups of genes, that were linked to the LIFO network. The top gene variants in these clusters (3,934 total) were from genes that regulate immune cell trafficking, inflammation, and neurogenesis, genes that have been linked to blood pressure, sleep duration, and cognitive performance, and genes located in a genetic region associated with Alzheimer’s disease and other neurodegenerative disorders. In total, these results suggest that individuals with specific variants of the genes identified may have a LIFO network more vulnerable to disease and aging.

Regarding modifiable risk factors, the authors found that 12 of the 15 modifiable risk factor categories had at least one factor that significantly affected the LIFO brain network. Taken together, these 12 factors significantly explained 1.5% of the vulnerability of the network after age and sex were removed. Of these factors, the authors determined that diabetes, alcohol intake, and pollution by nitrogen dioxide were the most harmful to the LIFO regions. This suggests that individuals who have been diagnosed with diabetes, who consume copious amounts of alcohol, or who have high exposure to nitrogen dioxide pollution may be at a higher risk of cognitive degeneration in these brain regions. 

What's the impact?

This study is the first to identify specific genetic and modifiable risk factors associated with the LIFO brain network, which contains the high-functioning regions most vulnerable to decline by aging and disease. Determining the specific gene variants and modifiable risk factors that are associated with LIFO regions may help to identify individuals at higher risk for cognitive decline. Recognizing these factors will allow patients and providers more time to protect against potential decline and help explain the biological mechanisms behind degeneration. 

A New Model of Synaptic Plasticity: Neurons Depend on Their Neighbours for Learning

Post by Laura Maile

The takeaway

Plasticity, or strengthening of synapses over time, is dependent on a complex interaction involving both excitatory and inhibitory inputs from a network of nearby neurons. When we learn, we rely not on single inputs and outputs, but on dynamic communication between networks of neurons.  

What's the science?

Synaptic plasticity is the collection of changes to both excitatory and inhibitory connections between neurons that occur when we learn. Historically, the understanding has been that this plasticity functions at the single synapse level, relying on the activity of a single presynaptic neuron and the response of its partner across the synapse. Hebb’s theory of learning states that when a presynaptic neuron repeatedly fires and activates a neighboring neuron, their connection is strengthened. More recent evidence has shown that learning and plasticity are more complex, integrating both excitatory and inhibitory information from other neighboring synapses, dependent on the circuitry of nearby networks. Scientists have not yet agreed upon a framework to explain this concept of interdependent synaptic plasticity in biological models. This week in Nature Neuroscience, Agnes and colleagues describe a new model of synaptic plasticity that relies on the activity of multiple neighboring synapses.  

How did they do it?

The authors created a theoretical model consisting of a set of rules integrating the timing, strength, distance, and identity of excitatory and inhibitory inputs to describe the interaction between sets of neurons during learning. They first utilized two excitatory neurons isolated from other inputs and presented different stimulation patterns to the pre and post-synaptic neurons. To increase the system's complexity, they introduced neighboring synapses to the same excitatory postsynaptic neuron. Next, to determine the influence of multiple neighboring inputs, they modeled a single postsynaptic neuron with several presynaptic inputs spaced apart uniformly. The authors then sought to understand how synaptic plasticity influences the receptive fields of neurons, or their ability to respond to different stimuli. To do this, they simulated a neuron receiving eight different inputs, to mimic the input of eight different frequencies of sound. They mimicked the learning period by using inhibitory inputs to gate or limit the time the postsynaptic neuron could be influenced by excitatory inputs. They next wanted to test their rules on a more spatially and structurally complex dendritic tree, mimicking the tree-like organization of synapses onto a single neuron. To do this, they connected dendritic compartments that could be independently activated, to a single neuron.  

What did they find?

Their model demonstrated that long-term potentiation (LTP), the process of synaptic strengthening upon repeated activation of a presynaptic neuron, can be initiated by the presynaptic neuron, and increases when the pre and postsynaptic neurons fire in synchrony. When neighboring synapses also increase their firing, the postsynaptic neuron showed increased LTP, in a fashion dependent on time and distance from the postsynaptic neuron. When multiple neighboring neurons targeted the same postsynaptic neuron at equal distances, their distance and ability to influence one another drove competition.

In experiments modeling receptive fields, the authors demonstrated that learning occurred when the gating inhibitory neurons were shut down, allowing the postsynaptic neuron to experience strong stimulation by excitatory inputs. With dendritic tree modeling, they showed that the plasticity of the excitatory synapses was dependent on inhibitory gating, distance from the cell body, and co-activity of surrounding inputs. Inhibition was found to directly influence excitatory synaptic plasticity. Inhibitory plasticity is slower than excitatory, and has strong control over excitatory plasticity, preventing too much change in excitatory weights and stabilizing learning. Finally, they created a model of a neuronal network, in which setpoints are used to balance LTP with synaptic weakening, creating a stable network that allows for learning without too much runaway excitation.  

What's the impact?

This study found that synaptic plasticity depends on a network of nearby synapses. The model developed can help explain how clusters of synapses develop and strengthen into stable systems. This work helps neuroscientists better represent and understand the complex dynamics of neural connections that change over time as we learn.  

Access the original scientific publication here.