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Virtual Brain Gives Insights Into Memory Deficits in Depression

Summary: Researchers have developed a new computational model of major depressive disorder. The model reveals older memories, as well as short term memories, are affected by major depressive disorder. Researchers say how long the memory deficits go back depends on how long the depressive episode lasts.

Source: RUB.

During a depressive episode the ability of the brain to form new brain cells is reduced. Scientists of the Ruhr-Universität Bochum examined how this affects the memory with a computational model. It was previously known that people in an acute depressive episode were less likely to remember current events. The computational model however suggests that older memories were affected as well. How long the memory deficits reach back depends on how long the depressive episode lasts.

The team around the computational neuroscientist Prof Dr Sen Cheng published their findings in the journal PLOS ONE on 7th June 2018.

Computational model simulates a depressive brain

In major depressive disorder patients may suffer from such severe cognitive impairments that, in some cases, are called pseudodementia. Unlike in the classic form of dementia, in pseudodementia memory recovers when the depressive episode ends. To understand this process, the scientists from Bochum developed a computational model that captures the characteristic features of the brain of a patient with depressions. They tested the ability of the model to store and recall new memories.

As is the case in patients, the simulation alternated between depressive episodes and episodes without any symptoms. During a depressive episode, the brain forms fewer new neurons in the model.

a head with computer wires and an exposed brain

The computational model not only showed deficits in recalling current events, it also struggled with memories that were collected before the depressive episode. NeuroscienceNews.com image is in the public domain.

Whereas in previous models, memories were represented as static patterns of neural activity, the model developed by Sen Cheng and his colleagues views memories as a sequence of neural activity patterns. “This allows us not only to store events in memory but also their temporal order,” says Sen Cheng.

Impact on brain stronger than thought

The computational model was able to recall memories more accurately, if the responsible brain region was able to form many new neurons, just like the scientists expected. However, if the brain region formed fewer new brain cells, it was harder to distinguish similar memories and to recall them separately.

The computational model not only showed deficits in recalling current events, it also struggled with memories that were collected before the depressive episode. The longer the depressive episode lasted the further the memory problems reached back.

„So far it was assumed that memory deficits only occur during a depressive episode,” says Sen Cheng. “If our model is right, major depressive disorder could have consequences that are more far reaching. Once remote memories have been damaged, they do not recover, even after the depression has subsided.”

About this neuroscience research article

Funding: Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Stiftung Mercator funded this study.

Source: Sen Cheng – RUB
Publisher: Organized by NeuroscienceNews.com.
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Open access research for “The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory” by Jing Fang, Selver Demic, and Sen Cheng in PLOS ONE. Published June 7 2018.
doi:10.1371/journal.pone.0198406

Cite This NeuroscienceNews.com Article
RUB “Virtual Brain Gives Insights Into Memory Deficits in Depression.” NeuroscienceNews. NeuroscienceNews, 8 June 2018.
<http://neurosciencenews.com/virtual-brain-depression-memory-9292/>.
RUB (2018, June 8). Virtual Brain Gives Insights Into Memory Deficits in Depression. NeuroscienceNews. Retrieved June 8, 2018 from http://neurosciencenews.com/virtual-brain-depression-memory-9292/
RUB “Virtual Brain Gives Insights Into Memory Deficits in Depression.” http://neurosciencenews.com/virtual-brain-depression-memory-9292/ (accessed June 8, 2018).

Abstract

The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory

Major depressive disorder (MDD) is associated with an impairment of episodic memory, but the mechanisms underlying this deficit remain unclear. Animal models of MDD find impaired adult neurogenesis (AN) in the dentate gyrus (DG), and AN in DG has been suggested to play a critical role in reducing the interference between overlapping memories through pattern separation. Here, we study the effect of reduced AN in MDD on the accuracy of episodic memory using computational modeling. We focus on how memory is affected when periods with a normal rate of AN (asymptomatic states) alternate with periods with a low rate (depressive episodes), which has never been studied before. Also, unlike previous models of adult neurogenesis, which consider memories as static patterns, we model episodic memory as sequences of neural activity patterns. In our model, AN adds additional random components to the memory patterns, which results in the decorrelation of similar patterns. Consistent with previous studies, higher rates of AN lead to higher memory accuracy in our model, which implies that memories stored in the depressive state are impaired. Intriguingly, our model makes the novel prediction that memories stored in an earlier asymptomatic state are also impaired by a later depressive episode. This retrograde effect exacerbates with increased duration of the depressive episode. Finally, pattern separation at the sensory processing stage does not improve, but rather worsens, the accuracy of episodic memory retrieval, suggesting an explanation for why AN is found in brain areas serving memory rather than sensory function. In conclusion, while cognitive retrieval biases might contribute to episodic memory deficits in MDD, our model suggests a mechanistic explanation that affects all episodic memories, regardless of emotional relevance.

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