Mental fatigue in stress-related exhaustion disorder: Structural brain correlates, clinical characteristics and relations with cognitive functioning

Gavelin, Hanna Malmberg; Neely, Anna Stigsdotter; Dunås, Tora; Eskilsson, Therese; Järvholm, Lisbeth Slunga; Boraxbekk, Carl-Johan

doi:10.1016/j.nicl.2020.102337

Cited by 23 publications

(18 citation statements)

References 53 publications

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“…Their understanding and categorising might be useful in the process of modelling information processing capabilities. The level of mental fatigue and its influence on the brain cognitive capability is the subject of numerous research articles [ 2 , 3 ]. Mental fatigue might lead to a decrease of brain cognitive system performance in terms of perception, attention, analysing, and planning [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021

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The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). Understanding cognitive workload is of great importance in analysing human mental fatigue and the performance of intellectual tasks. It is also essential in the context of explanation of the brain cognitive process. Eight three-class classification machine learning models were constructed and analysed. Furthermore, the technique of interpretable machine learning model was applied to obtain the measures of feature importance and its contribution to the brain cognitive functions. The measures allowed improving the quality of classification, simultaneously lowering the number of applied features to six or eight, depending on the model. Moreover, the applied method of explainable machine learning provided valuable insights into understanding the process accompanying various levels of cognitive workload. The main classification performance metrics, such as F1, recall, precision, accuracy, and the area under the Receiver operating characteristic curve (ROC AUC) were used in order to assess the quality of classification quantitatively. The best result obtained on the complete feature set was as high as 0.95 (F1); however, feature importance interpretation allowed increasing the result up to 0.97 with only seven of 20 features applied.

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Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021

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“…Patients with high mental fatigue that is related to chronic stress had significantly lower caudate and putamen volumes, compared with the low-moderate mental fatigue group (Gavelin et al, 2020). Even when groups were controlled for perceived stress and depression conditions, the difference in volumes was still significant (p=004 for caudate and p=003 for putamen).…”

Section: Stressmentioning

confidence: 84%

“…While investigating chronic stress and associated mental fatigue Gavelin et al (2020) mentioned that observation of significantly reduced volumes of the gray matter of the ACC (anterior cingulate cortex) was present (Blix et al, 2013).…”

Section: Stressmentioning

confidence: 99%

Do patients with chronic stress and patients with ADHD develop the same brain structures?

Obukhova¹

2021

Preprint

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Attention deficit hyperactivity disorder (ADHD hereafter) is a disorder associated with the impaired development of the child's nervous system and can be prolonged to adulthood with the symptoms such as impulsivity, inattention, issues with remembering of information. Stress, especially chronic stress, also negatively affects brain function and causes nearly the same symptoms as ADHD. The symptoms are almost parallel. Recent research is shedding more light on the relationship between stress and ADHD. There might be a combination of genetic and environmental factors that influence ADHD onset, and, interestingly, the majority of environmental factors are related to chronic stress during pregnancy or in life. The aim of this review is evaluating the similarities and differences between ADHD and chronic stress, and analyze the nature of alterations caused by ADHD and chronic stress. Research findings showed that in several brain areas both ADHD and stress create similar structure alterations (mainly reduction) (Saenz et al., 2019; Blix et al., 2013). But that data is not 100% consistent and may need more research. Also, in the amygdala (Hoogman et al., 2017; Perlov et al., 2008) and in the hippocampus (Hoogman et al., 2017; Plessen et al., 2006), we see not consistent data and even have an opposite direction of data that needs further review and might explain that these brain structures both are not properly functioning but in different ways

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“…25,34 This finding could easily explain the link between IAD and cognitive fatigue, as volumetric changes in the striatum, anterior cingulate cortex and thalamus are responsible for both impaired response inhibition leading to cognitive fatigue and IAD. According to Gavelin et al, 26 participants with a high level of cognitive fatigue have a smaller caudate nucleus and putamen than participants with low-moderate cognitive fatigue. Furthermore, the authors noted that an indirect negative effect of caudate nucleus volume on working memory performance was found to be the reason for abnormal response inhibition and cognitive fatigue as a result.…”

Section: Wwwmedigraphicorgmxmentioning

confidence: 99%

“…Thus, fatigue, including general fatigue, sleep/ rest and cognitive fatigue, may be of special importance in children with excessive Internet use. 26 According to Krabbe et al, 27 patients with exhaustion complain about mental tiredness during and after cognitive testing together with impaired performance in tasks involving executive functioning and complex attention. Another concern is the condition of clinical burnout, where patients reveal not only cognitive deficits but also impaired performance www.medigraphic.org.mx on cognitive tests, as the process seems to be effortful and fatiguing for them.…”

Section: Introductionmentioning

confidence: 99%

Trastorno de adicción a Internet y comorbilidades en niños y adolescentes

Nadiradze¹,

Khachapuridze²,

Kvachadze³

et al. 2021

Neurología, Neurocirugía Y Psiquiatría

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Introduction: Internet addiction disorder (IAD) is characterized by an individual's inability to control his/her Internet use, which may result in marked distress and functional impairment. Systematic reviews show that excessive screen-time is negatively associated with well-being and positively associated with reduced quality of life in young people. There is growing evidence that IAD is related to comorbidities such as depression but relatively little is known about fatigue in adolescents with IAD. Material and methods: We studied 94 participants with IAD and 88 controls, all aged 12-17 years. Depression was assessed by the Beck depression inventory scale (BDI-II, Georgian version), and fatigue by the pediatric quality of life initiative (PEDS QL, Georgian version) multidimensional fatigue scale. Results: Adolescents with severe IAD are 5.63 times more likely to show symptoms of moderate or severe depression than children with mild or moderate Internet addiction. Those with severe IAD showed 6.62 times more cognitive fatigue, 7.81 times higher sleep/rest fatigue and 11.11 times higher general fatigue than children with mild and moderate Internet addiction. Conclusions: IAD can lead to depression and fatigue, which can affect adolescents' psychological and social wellbeing. Mechanisms for prevention and ongoing support are needed for adolescents and their families. Further research is needed to clarify the relationship between IAD and depression, that is, does Internet addiction lead to depression or is depression itself a risk factor for IAD? A further limitation is that IAD was treated as one entity rather than two distinct types (generalized and specific IAD).

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Mental fatigue in stress-related exhaustion disorder: Structural brain correlates, clinical characteristics and relations with cognitive functioning

Cited by 23 publications

References 53 publications

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

Do patients with chronic stress and patients with ADHD develop the same brain structures?

Trastorno de adicción a Internet y comorbilidades en niños y adolescentes

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