Neuroimaging Correlates of Depression after Traumatic Brain Injury: A Systematic Review

Medeiros, Gustavo C.; Twose, Claire; Weller, Alexandra; Dougherty, John W.; Goes, Fernando S.; Sair, Haris I.; Smith, Gwenn S.; Roy, Durga

doi:10.1089/neu.2021.0374

Cited by 16 publications

(11 citation statements)

References 74 publications

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“…This heterogeneity may originate from the varied biomechanical parameters of blast/impact, such as force direction, force magnitude, presence of rigid blast-reflecting surfaces, presence and type of protective gear, and many other amplifying or mitigating factors. Amidst the widely heterogeneous neuroimaging findings in the literature on TBI depression, the most consistent connectivity changes have been found between the limbic system, prefrontal cognitive control regions, and prefrontal emotion regulation regions ( 20 , 42 ). Our results are broadly consistent with these previous findings, but our large sample size and advanced methods provide better specificity.…”

Section: Discussionmentioning

confidence: 94%

“…Our results are consistent with recent reports that individualized brain network mapping can better explain other syndromes such as psychosis ( 16 ). The use of individualized brain network mapping may explain why we were able to unmask neurophysiological substrates in TBI despite its relative neuroanatomical and neurofunctional heterogeneity ( 20 , 41 ). This heterogeneity may originate from the varied biomechanical parameters of blast/impact, such as force direction, force magnitude, presence of rigid blast-reflecting surfaces, presence and type of protective gear, and many other amplifying or mitigating factors.…”

Section: Discussionmentioning

confidence: 99%

“…Whereas the cognitive function subserved by different restingstate networks remains a topic of debate, specific networks have been consistently implicated in specific disorders. Both TBI and MDD are associated with abnormalities in the dorsal attention network (DAN), which is involved in externally oriented attention switching, and the default mode network (DMN), which is involved in internally oriented reflection and rumination (17)(18)(19)(20)(21)(22). In TBI, this dysfunctional interaction may contribute to impaired voluntary control of goal-directed attentional resources (23).…”

Section: Introductionmentioning

confidence: 99%

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Precision functional MRI mapping reveals distinct connectivity patterns for depression associated with traumatic brain injury

et al. 2023

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Depression associated with traumatic brain injury (TBI) is believed to be clinically distinct from primary major depressive disorder (MDD) and may be less responsive to conventional treatments. Brain connectivity differences between the dorsal attention network (DAN), default mode network (DMN), and subgenual cingulate have been implicated in TBI and MDD. To characterize these distinctions, we applied precision functional mapping of brain network connectivity to resting-state functional magnetic resonance imaging data from five published patient cohorts, four discovery cohorts ( n = 93), and one replication cohort ( n = 180). We identified a distinct brain connectivity profile in TBI-associated depression that was independent of TBI, MDD, posttraumatic stress disorder (PTSD), depression severity, and cohort. TBI-associated depression was independently associated with decreased DAN–subgenual cingulate connectivity, increased DAN-DMN connectivity, and the combined effect of both. This effect was stronger when using precision functional mapping relative to group-level network maps. Our results support the possibility of a physiologically distinct “TBI affective syndrome,” which may benefit from individualized neuromodulation approaches to target its distinct neural circuitry.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Precision functional MRI mapping reveals distinct connectivity patterns for depression associated with traumatic brain injury

et al. 2023

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“…Numerous brain regions are involved in regulating mood, including pre-frontal, insular and anterior cingulate cortex, hippocampus, several amygdala subnuclei, nucleus accumbens, and the bed nucleus of the stria terminalis. 11 , 32–35 The precise brain regions that cause depression, fear, and anxiety after mild TBI, and the nature of the alterations in them, are not well understood. Thus, determining how raloxifene acts on specific regions and cells within the brain to yield functional rescue, though desirable, is beyond the scope of the current study.…”

Section: Discussionmentioning

confidence: 99%

Raloxifene Mitigates Emotional Deficits after Mild Traumatic Brain Injury in Mice

Honig

Mar

Moore

et al. 2022

Neurotrauma Reports

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Persons with mild traumatic brain injury (TBI) often exhibit persistent emotional impairments, particularly depression, fearfulness, and anxiety, that significantly diminish quality of life. Studying these mood disorders in animal models of mild TBI can help provide insight into possible therapies. We have previously reported that mice show increased depression, fearfulness, and anxiety, as well as visual and motor deficits, after focal cranial blast and that treatment with the cannabinoid type 2 receptor (CB2) inverse agonist, SMM-189, reduces these deficits. We have further shown that raloxifene, which is U.S. Food and Drug Administration approved as an estrogen receptor modulator to treat osteoporosis, but also possesses CB2 inverse agonism, yields a similar benefit for visual deficits in this model of TBI. Here, we have extended our studies of raloxifene benefit and show that it similarly reverses depression, fearfulness, and anxiety after focal cranial blast TBI in mice, using standard assays of these behavioral end-points. These results indicate the potential of raloxifene in the broad rescue of deficits after mild TBI and support phase 2 efficacy testing in human clinical trials.

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“…This selection stage yielded a total of 83 papers. Similar to previous surveys, this survey mainly focused on neuroimaging data [93][94][95][96]. This review also expands on the work by Wen, Thibeau-Sutre, Diaz-Melo, Samper-González, Routier, Bottani, Dormont, Durrleman, Burgos and Colliot [89], which focuses on convolutional neural networks, to a broader range of supervised and unsupervised neural networks, including recent advances in graph and geometric neural networks.…”

mentioning

confidence: 86%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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Neuroimaging Correlates of Depression after Traumatic Brain Injury: A Systematic Review

Cited by 16 publications

References 74 publications

Precision functional MRI mapping reveals distinct connectivity patterns for depression associated with traumatic brain injury

Precision functional MRI mapping reveals distinct connectivity patterns for depression associated with traumatic brain injury

Raloxifene Mitigates Emotional Deficits after Mild Traumatic Brain Injury in Mice

A Survey of Deep Learning for Alzheimer’s Disease

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