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Resting-state neural signatures of depressive symptoms in acute HIV.

ABSTRACT: Depressive symptoms are often elevated in acute and chronic HIV. Previous neuroimaging research identifies abnormalities in emotion-related brain regions in depression without HIV, including the anterior cingulate cortex (ACC) and amygdala. However, no studies have examined the neural signatures of depressive symptoms in acute HIV infection (AHI). Seed-based voxelwise resting-state functional connectivity (rsFC) for affective seed regions of interest (pregenual ACC, subgenual ACC [sgACC], bilateral amygdala) was computed for 74 Thai males with AHI and 30 Thai HIV-uninfected controls. Group analyses compared rsFC of ACC and amygdala seed regions between AHI and uninfected control groups. Within the AHI group, voxelwise regression analyses investigated the relationship between depressive symptoms and rsFC for these affective seed regions. Group analyses revealed alterations in rsFC of the amygdala in AHI versus uninfected controls. Depressive symptoms associated with decreased rsFC between ACC regions and posterior cingulate/precuneus, medial temporal, and lateral parietal regions in AHI. Symptoms of depression also correlated to increased rsFC between ACC regions and lateral prefrontal cortex, sgACC, and cerebellum in AHI. Similar to the ACC, depressive symptoms associated with decreased rsFC between amygdala and precuneus. Of blood biomarkers, only HIV RNA inversely correlated with rsFC between posterior sgACC and left uncus. We found that depressive symptoms in AHI associate with altered rsFC of ACC and amygdala regions previously implicated in depression. Longitudinal research in this cohort will be necessary to determine whether these early alterations in rsFC of affective network regions are related to persistent depressive symptoms after combination antiretroviral therapy.

SUBMITTER: Philippi CL

PROVIDER: S-EPMC7261250 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Resting-state neural signatures of depressive symptoms in acute HIV.

Philippi Carissa L CL Reyna Leah L Nedderman Laura L Chan Phillip P Samboju Vishal V Chang Kevin K Phanuphak Nittaya N Ratnaratorn Nisakorn N Hellmuth Joanna J Benjapornpong Khunthalee K Dumrongpisutikul Netsiri N Pothisri Mantana M Robb Merlin L ML Ananworanich Jintanat J Spudich Serena S Valcour Victor V Paul Robert R

Journal of neurovirology 20200127 2

Depressive symptoms are often elevated in acute and chronic HIV. Previous neuroimaging research identifies abnormalities in emotion-related brain regions in depression without HIV, including the anterior cingulate cortex (ACC) and amygdala. However, no studies have examined the neural signatures of depressive symptoms in acute HIV infection (AHI). Seed-based voxelwise resting-state functional connectivity (rsFC) for affective seed regions of interest (pregenual ACC, subgenual ACC [sgACC], bilate ...[more]

PMID: 31989446

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Project description:Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

Dataset Information

Resting-state neural signatures of depressive symptoms in acute HIV.

Publications

Resting-state neural signatures of depressive symptoms in acute HIV.

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