Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment

Méndez, María Andreina; Zuluaga, Pilar; Hornero, Roberto; Gómez, Carlos; Escudero, Javier; Rodriguez-Palancas, A; Alonso, Tomás Ortiz; Fernández, Alberto

doi:10.1177/0269881111408966

Cited by 102 publications

(64 citation statements)

References 38 publications

(46 reference statements)

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“…In previous studies, we found that age and gender might be modifying factors of LZC values (Fernández et al, 2011a;Méndez et al, 2011). Thus, our study began with an exploratory analysis by describing data according to these two variables.…”

Section: Age and Gender Effects On Lzcmentioning

confidence: 98%

“…No significant differences in terms of age were found between males (42.26 ± 21.08) and females (45.11 ± 22.05). All participants included in the current investigation have been described in some of our previous studies (Fernández et al, 2002;Fernández et al, 2009;Fernández et al, 2010;Fernández et al, 2011a;Méndez et al, 2011;Solesio et al, 2009). …”

Section: Participantsmentioning

confidence: 99%

“…Thus, statistical analyses were performed with 148 LZC scores per subject. As in previous studies (Fernández et al, 2009;Fernández et al, 2010;Fernández et al, 2011a;Méndez et al, 2011), the initial 148 LZC values were averaged into five regions: anterior, central, left lateral, right lateral and posterior, which are included as default sensor groups in the 4D-neuroimaging source analysis software (see Fig. 1).…”

Section: Meg Data Reduction and Analysismentioning

confidence: 99%

“…Considering preceding investigations, clinical populations of different pathologies exhibited significantly higher or lower complexity values than controls, but such statistical difference only reveals a percentage of the actual dissimilarity between the pathological and the healthy states. Recent studies proved that pathologies such us mild cognitive impairment, depression, schizophrenia or ADHD (see Fernández et al, 21 2009;Fernández et al, 2010;Fernández et al, 2011a;Méndez et al, 2011) not only cause an 'abnormal' increase or decrease of oscillatory complexity values but also actually 'break' the 'normal' pattern of complexity evolution as a function of age. For instance, patients with major depression presented higher complexity values than controls, and these values failed to show the 'normal' increase with age that would be expected within the age range of the sample.…”

Section: Significancementioning

confidence: 99%

“…LZC has been used to analyse EEG and MEG signals in patients with Alzheimer's disease Fernández et al, 2010;Gómez et al, 2006), attention deficit-hyperactivity disorder (ADHD) (Fernández et al, 2009), depression and schizophrenia (Li et al, 2008;Fernández et al, 2011a;Méndez et al, 2011;) as well as to measure the depth of anaesthesia (Zhang et al, 2001), or to study epileptic seizures (Radhakrishnan and Gangadhar, 1998). The increasing clinical use of LZC and other estimates of oscillatory complexity is the main reason to carry out a normative study where the 'normal' behaviour of complexity values is defined according to age and gender influences in a large population.…”

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confidence: 99%

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Brain oscillatory complexity across the life span

Fernández

Zuluaga

Abásolo

et al. 2012

Clinical Neurophysiology

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Objective: Considering the increasing use of complexity estimates in neuropsychiatric populations, a normative study is critical to define the 'normal' behaviour of brain oscillatory complexity across the life span. Method:This study examines changes in resting-state magnetoencephalogram (MEG) complexity -quantified with the Lempel-Ziv complexity (LZC) algorithm -due to age and gender in a large sample of 222 (100 males/122 females) healthy participants with ages ranging from 7 to 84 years.Results: A significant quadratic (curvilinear) relationship (p < 0.05) between age and complexity was found, with LZC maxima being reached by the sixth decade of life.Once that peak was crossed, complexity values slowly decreased until late senescence.Females exhibited higher LZC values than males, with significant differences in the anterior, central and posterior regions (p < 0.05). Conclusions:These results suggest that the evolution of brain oscillatory complexity across the life span might be considered a new illustration of a 'normal' physiological rhythm.Significance: Previous and forthcoming clinical studies using complexity estimates might be interpreted from a more complete and dynamical perspective. Pathologies not 3 only cause an 'abnormal' increase or decrease of complexity values but they actually 'break' the 'normal' pattern of oscillatory complexity evolution as a function of age.Keywords: Life Span, Ageing, Complexity, Brain Development, White Matter Development Highlights 1. A significant quadratic (curvilinear) relationship between age and oscillatory complexity exists, with complexity maxima reached by the sixth decade of life.2. As in previous studies, females exhibit higher complexity values than males, at least in some brain regions.3. The evolution of oscillatory complexity across the life span is interpreted as a physiological rhythm which is altered by several brain pathologies.

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Section: Age and Gender Effects On Lzcmentioning

confidence: 98%

Section: Participantsmentioning

confidence: 99%

Section: Meg Data Reduction and Analysismentioning

confidence: 99%

Section: Significancementioning

confidence: 99%

mentioning

confidence: 99%

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Brain oscillatory complexity across the life span

Fernández

Zuluaga

Abásolo

et al. 2012

Clinical Neurophysiology

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Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

et al. 2020

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IMPORTANCE Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression. OBJECTIVE To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment. INTERVENTIONS All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment. MAIN OUTCOMES AND MEASURES The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%. RESULTS Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%). CONCLUSIONS AND RELEVANCE These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

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Resting‐State Electroencephalogram Complexity Is Associated With Oral Ketamine Treatment Response: A Bayesian Analysis of Lempel–Ziv Complexity and Multiscale Entropy

Mitchell,

Anijärv,

Can

et al. 2024

Brain and Behavior

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IntroductionSubanesthetic doses of ketamine are a promising novel treatment for suicidality; however, the evidence for predictive biomarkers is sparse. Recently, measures of complexity, including Lempel–Ziv complexity (LZC) and multiscale entropy (MSE), have been implicated in ketamine's therapeutic action. We evaluated electroencephalogram (EEG)‐derived LZC and MSE differences between responders and nonresponders to oral ketamine treatment.MethodsA total of 31 participants received six single, weekly (titrated) doses of oral racemic ketamine (0.5–3 mg/kg) and underwent EEG scans at baseline (Week 0), post‐treatment (Week 6), and follow‐up (Week 10). Resting‐state (eyes closed and open) recordings were processed in EEGLAB, and complexity metrics were extracted using the Neurokit2 package. Participants were designated responders or nonresponders by clinical response (Beck Suicide Scale [BSS] score reduction of ≥ 50% from baseline to the respective timepoint or score ≤ 6) and then compared in terms of complexity across resting‐state conditions and time.ResultsEmploying a Bayesian mixed effects model, we found strong evidence that LZC was higher in the eyes‐open compared to eyes‐closed condition, as were MSE scales 1–3. At a global level, responders displayed elevated eyes‐open baseline complexity compared to nonresponders, with these values decreasing from baseline to post‐treatment (Week 6) in responders only. Exploratory analyses revealed that the elevated baseline eyes‐open LZC in responders was spatially localized to the left frontal lobe (F1, AF3, FC1, and F3).ConclusionEEG‐complexity metrics may be sensitive biomarkers for evaluating and predicting oral ketamine treatment response, with the left prefrontal cortex bein a possible treatment response region.

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Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment

Cited by 102 publications

References 38 publications

Brain oscillatory complexity across the life span

Brain oscillatory complexity across the life span

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

Resting‐State Electroencephalogram Complexity Is Associated With Oral Ketamine Treatment Response: A Bayesian Analysis of Lempel–Ziv Complexity and Multiscale Entropy

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