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An investigation of EEG, genetic and cognitive markers of treatment response to antidepressant medication in patients with major depressive disorder: A pilot study
Abstract
The aim of this study was to investigate if biomarkers in QEEG, genetic and neuropsychological measures are suitable for the prediction of antidepressant treatment outcome in depression. Twenty-five patients diagnosed with major depressive disorder were assessed twice, pretreatment and at 8-wk follow-up, on a variety of QEEG and neuropsychological tasks. Additionally, cheek swab samples were collected to assess genetic predictors of treatment outcome. The primary outcome measure was the absolute decrease on the HAM-D rating scale. Regression models were built in order to investigate which markers contribute most to the decrease in absolute HAM-D scores. Patients who had a better clinical outcome were characterized by a decrease in the amplitude of the Auditory Oddball N1 at baseline. The 'Met/Met' variant of the COMT gene was the best genetic predictor of treatment outcome. Impaired verbal memory performance was the best cognitive predictor. Raised frontal Theta power was the best EEG predictor of change in HAM-D scores. A tentative integrative model showed that a combination of N1 amplitude at Pz and verbal memory performance accounted for the largest part of the explained variance. These markers may serve as new biomarkers suitable for the prediction of antidepressant treatment outcome.
... Indicators of θ activity were found to be linked with the effects of treatment for depression with various antidepressants [19,[28][29][30][31] and electroconvulsive therapy [27] (ECT), though differently in different studies. Thus, Knott et al. [19] studied a group of patients receiving imipramine for four weeks and found that initial absolute spectral θ-activity power (regardless of EEG lead) in 13 responders was lower than in 16 nonresponders. ...
... In other studies, good therapeutic responses in patients with depression treated with various antidepressants were, conversely, signifi cantly associated with initially greater absolute EEG θ-rhythm spectral power in the frontal leads [30], and also with greater current density in the θ range in the orbitofrontal cortex and rostral part of the cingulate gyrus found by low-resolution EEG tomography (LORETA) [31]. ...
... Baskaran et al. [17] found that treatment with escitalopram in 18 responders (of 44 included in the study of patients with depression) decreased cordance two weeks after starting treatment courses but increased it in 26 nonresponders. The sensitivity of this eight narrow frequency subranges: δ (2-4 Hz), θ1 (4-6 Hz), θ2 (6-8 Hz), α1 (8-9 Hz), α2 (9-11 Hz), α3 (11-13 Hz), β1 (13)(14)(15)(16)(17)(18)(19)(20), and β2 (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Quantitative clinical evaluation of the state of patients with depression and depressive-delusional disorders in the framework of paroxysmal schizophrenia was performed using the Hamilton scale (HDRS), while evaluation in patients with manic-delusional disorders used the Young Mania rating Scale (YMRS) and evaluation in patients with hallucinatory-delusional disorders used the positive and negative symptoms scale (PANSS). ...
This literature review presents data on an EEG biomarker for prognostication (predictors) of therapeutic responses in patients with different types of mental pathology. Quantitative electroencephalogram (EEG) indicators recorded before courses of treatment are shown to reflect not only the ongoing functional state of the patient’s brain, but also its adaptive resources in terms of the potential for and magnitude of responses to treatment. The EEG indicators of therapeutic responses found in patients with depression, schizophrenia, and various other mental disorders have quite high predictive capacity, sensitivity, and specificity for identifying responders and nonresponders, and provide qualitative predictions for a patient’s state after treatment courses, and also help the physician select medications for optimum therapy.
... For example, patients who did not respond to antidepressants have been characterized by relatively elevated theta power at rest, 18,19 although the reverse outcome of relative reduced theta has also been observed. 20 Using source localization, theta activity relevant to predicting response among those taking fluoxetine hydrochloride or venlafaxine hydrochloride has been localized to the rostral anterior cingulate and medial orbitofrontal regions. 14 A distinct profile of alpha power has been associated with antidepressant response. ...
... At each electrode, the absolute power and the relative power were computed using the Simpson rule for the frequency ranges of delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-100 Hz). Two additional features were computed: a frontal alpha asymmetry feature by subtracting alpha power for a left scalp site (F3) from the homologous right site (F4) and a betaalpha ratio feature by taking the ratio of the beta features at each of the sites with the corresponding alpha features. ...
... Previous studies, with few exceptions, 35 have focused on using EEG features to predict response or remission, which are defined by differences in summed symptom scores, 24,36,37 and have yielded mixed outcomes. 20,22 Electroencephalographic features that predict the change in summed symptom scores may not be replicated across populations of depression in which the primary depressive symptoms are highly heterogenous; thus, our findings offer an indication that the use of individual symptoms may be one means to address the replication gap in evaluating the potential value of EEG biomarkers of treatment outcomes in future studies. This approach might also help determine if EEG features add value to the previous suggestion that symptoms may have a differential rate of improvement. ...
Importance
Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.
Objective
To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.
Design, Setting, and Participants
This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019.
Exposures
Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180).
Main Outcomes and Measures
The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index.
Results
The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]).
Conclusions and Relevance
This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings.
Trial Registration
ClinicalTrials.gov Identifier: NCT00693849
... The majority of work identifying neuroimaging predictors of treatment response has been done in Major Depressive Disorder (MDD) and pre-treatment ACC activity has been shown to consistently predict treatment outcomes. This has been demonstrated by studies using PET, fMRI and EEG across various tasks and has been consistently found as a response to different antidepressant treatments (Jaworska et al. 2023;Keedwell et al., 2010;Pizzagalli et al. 2018;Saxena et al., 2003;Spronk et al. 2011) as well as to Cognitive Behavioral Therapy (CBT) (Feurer et al. 2022;McGrath et al., 2014;Siegle et al., 2006Siegle et al., , 2012. Such findings may potentially be used to optimize treatment selection by differentiating between medication and psychotherapy response. ...
... The copyright holder for this preprint this version posted October 25, 2024. ; Saxena et al., 2003;Siegle et al., 2006Siegle et al., , 2012Spronk et al. 2011;Osinsky et al., 2017). EEG shows promise in clinical settings due to its greater temporal resolution and being a more portable and cost effective method of measuring brain activity compared to other imaging modalities. ...
Despite various interventions for anxiety disorders, effects can take months and many individuals do not respond. Activity in the anterior cingulate cortex is a consistent predictor of treatment outcomes, and can be measured by medial frontal theta (MFT) event-related potentials. This study used task-based electroencephalography and MFT to predict anxiety sensitivity treatment response at mid-treatment, 1-week post-treatment, and 6 months post-treatment. Results indicated that lower medial to lateral prefrontal theta phase synchrony was associated with greater symptom improvement.
... A six-week study of paroxetine in men with major depressive disorder (MDD) showed that in addition to improved mood symptoms, chronic treatment led to a reduction in alpha power and an increase in delta, theta and beta power, particularly at bilateral frontal areas (13). Baseline EEG markers, including N1 amplitudes to oddball trials and resting frontal theta power have also been associated with greater subsequent reductions of depressive symptoms over 8 weeks of treatment (14). ...
... However, the analysis of the midline clusters did not show any change in resting power following nicotine intervention for any of the frequency bands (all p > 0.1). There was a significant difference in resting beta power across the sites, which showed a reduction in beta power from frontal to central clusters, with the lowest power at the parietal cluster [F (2,14) = 3.96, p = 0.044, η 2 p = 0.36]. However, this difference in resting power across the scalp did not change with nicotine treatment (p = 0.3). ...
Late-life depression (LLD) is a debilitating condition that is associated with poor response to antidepressant medications and deficits in cognitive performance. Nicotinic cholinergic stimulation has emerged as a potentially effective candidate to improve cognitive performance in patients with cognitive impairment. Previous studies of nicotinic stimulation in animal models and human populations with cognitive impairment led to examining potential cognitive and mood effects of nicotinic stimulation in older adults with LLD. We report results from a pilot study of transdermal nicotine in LLD testing whether nicotine treatment would enhance cognitive performance and mood. The study used electroencephalography (EEG) recordings as a tool to test for potential mechanisms underlying the effect of nicotine. Eight non-smoking participants with LLD completed EEG recordings at baseline and after 12 weeks of transdermal nicotine treatment (NCT02816138). Nicotine augmentation treatment was associated with improved performance on an auditory oddball task. Analysis of event-related oscillations showed that nicotine treatment was associated with reduced beta desynchronization at week 12 for both standard and target trials. The change in beta power on standard trials was also correlated with improvement in mood symptoms. This pilot study provides preliminary evidence for the impact of nicotine in modulating cortical activity and improving mood in depressed older adults and shows the utility of using EEG as a marker of functional engagement in nicotinic interventions in clinical geriatric patients.
... Кроме того, использование ЭЭГ-маркеров в клинической практике в качестве скрининга терапевтической устойчивости пациентов к антидепрессантам обладает гораздо большим потенциалом по сравнению с менее доступными исследованиями биохимических и генетических маркеров. На сегодняшний день существует множество исследований, нацеленных на поиск потенциальных ЭЭГ-маркеров эффективности фармакотерапии депрессии [12][13][14][15]. Однако полученные данные остаются неполными и несколько противоречивыми. ...
... В ряде более ранних исследований в качестве возможных предикторов терапевтической чувствительности к антидепрессантам больных депрессией авторы рассматривали разные параметры тета-, альфа-и бета-ритмов ЭЭГ [12][13][14][15][16][17], что в целом согласуется с результатами, полученными в данном исследовании. Статистически значимо более высокие показатели Таблица 3. Данные ЭЭГ пациентов группы 1 и 2 до начала терапии Table 3. EEG Data of patients from groups 1 and 2 before the start of treatment ...
... Major depressive disorder (MDD) is a heterogeneous disease with widespread biopsychosocial causes, which explains why not all patients benefit from the same treatment ( Spronk et al., 2011 ). Further, there is an often-prolonged time of treatment with trial-and-error regarding the choice of intervention ( Baskaran et al., 2012 ). ...
... Due to its low cost, high temporal resolution, and high accessibility, electroencephalography (EEG) shows great promise as a useful biomarker in clinical settings. Systematic efforts have been made to identify such predictive biomarkers ( Olbrich and Arns, 2013 ;Spronk et al., 2011 ;Trivedi et al., 2016a ;Wade and Iosifescu, 2016 ;Williams et al., 2011 ), as summarized in Table 1 , several quantitative EEG (qEEG) biomarkers have shown potential to predict treatment outcome in MDD. Among those markers, the hemispheric asymmetry of alpha power (known as "alpha asymmetry") has been consistently reported to have prognostic value for improving treatment outcome. ...
Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response.
Registration number: NCT02869035.
... Darüber hinaus könnte die Normalisierung der oszillatorischen Aktivität bereits als Marker bzw. Prädiktor für den Behandlungserfolg herausgearbeitet werden [120][121][122][123]. Ein hohe Midline-Theta-Aktivität und höherfrequente Peaks in den Alpha-Oszillationen im quantitativen EEG scheinen als prädiktiv für eine positive Responsiveness auf die antidepressive Behandlung zu sein [120][121][122]. ...
... Darüber hinaus könnte die Normalisierung der oszillatorischen Aktivität bereits als Marker bzw. Prädiktor für den Behandlungserfolg herausgearbeitet werden [120][121][122][123]. Ein hohe Midline-Theta-Aktivität und höherfrequente Peaks in den Alpha-Oszillationen im quantitativen EEG scheinen als prädiktiv für eine positive Responsiveness auf die antidepressive Behandlung zu sein [120][121][122]. In einer anderen Studie zeigten Responder vor allem eine erhöhte Aktivität im niedrigen Theta-Bereich, währenddessen Non-Responder höherfrequente Theta-Aktivität aufwiesen [123]. ...
ZUSAMMENFASSUNG
Kognitive Kontrollprozesse sind wichtig, um eine Vielzahl an Alltagssituationen erfolgreich zu bewältigen. Bei psychischen Erkrankungen wie Schizophrenie oder Depression wurden Defizite in diesen Kontrollfunktionen beschrieben, wobei das kognitive Syndrom bei Depression in der klinischen Praxis häufig weniger Beachtung findet. In den vergangenen Jahren wurde den neuronalen Oszillationen als Korrelat für kognitive Kontrollleistungen vermehrt Aufmerksamkeit gewidmet und deren Veränderungen bei psychischen Erkrankungen untersucht. Die oszillatorische elektrische Hirnaktivität, also rhythmische Veränderungen neuronaler Aktivität, kann mit dem Elektroenzephalogramm (EEG) gemessen werden. In der Forschung kristallisierte sich dabei die oszillatorische Aktivität im Theta-Frequenzband als neuronales Korrelat von kognitiven Kontrollfunktionen und als wichtig für neuronale Kommunikation heraus. Befunde zeigen, dass Patienten mit Schizophrenie während der Lösung kognitiver Konflikte pathologische Veränderungen in diesem Frequenzband aufweisen. Bei Patienten mit Depression konnten diese Veränderungen noch nicht in solcher Deutlichkeit beschrieben werden. Der vorliegende Artikel führt in grundlegende Konzepte ein und beschreibt neuronale Oszillationen als Biomarker psychischer Erkrankungen, der zur Verbesserung der Diagnostik und Behandlung kognitiver Defizite beitragen könnte.
... Cerebral responses to nociceptive input can also be measured with electroencephalography (EEG), which stands out as a valuable tool as it is noninvasive, low cost, and easy to use [26,27]. In addition, it can provide reliable and relevant information during sensory stimulation by capturing the electrical activity of neuronal cell assemblies on a submillisecond time scale, and consequently represent the neuronal activity in real time [28,29]. Therefore, it is an ideal method to investigate somatosensory processing during experimental nociceptive stimulation paradigms. ...
... The main advantage of EEG over other neuroimaging techniques is its high temporal resolution [28,29]. Moreover, it employs a low-cost and portable device for which patients do not have to lie down to acquire data and are not restricted by metallic implants in the body or claustrophobia [38]. ...
Background
With its high temporal resolution, electroencephalography (EEG), a technique that records electrical activity of cortical neuronal cells, is a potentially suitable technique to investigate human somatosensory processing. By using EEG, the processing of (nociceptive) stimuli can be investigated, along with the functionality of the nociceptive pathway. Therefore, it can be applied in chronic pain patients to objectify whether changes have occurred in nociceptive processing. Typically, so-called event-related potential (ERP) recordings are used, where EEG signals are recorded in response to specific stimuli and characterized by latency and amplitude.
Objective
To summarize whether differences in somatosensory processing occur between chronic pain patients and healthy controls, measured with ERPs, and determine whether this response is related to the subjective pain intensity.
Design
Systematic review.
Setting and Methods
PubMed, Web of Science, and Embase were consulted, and 18 case–control studies were finally included.
Subjects
The chronic pain patients suffered from tension-type headache, back pain, migraine, fibromyalgia, carpal tunnel syndrome, prostatitis, or complex regional pain syndrome.
Results
Chronic neuropathic pain patients showed increased latencies of the N2 and P2 components, along with a decreased amplitude of the N2-P2 complex, which was also obtained in FM patients with small fiber dysfunction. The latter also showed a decreased amplitude of the N2-P3 and N1-P1 complex. For the other chronic pain patients, the latencies and the amplitudes of the ERP components did not seem to differ from healthy controls. One paper indicated that the N2-P3 peak-to-peak amplitude correlates with the subjective experience of the stimulus.
Conclusions
Differences in ERPs with healthy controls can mostly be found in chronic pain populations that suffer from neuropathic pain or where fiber dysfunction is present. In chronic pain populations with other etiological mechanisms, limited differences were found or agreed upon across studies.
... In addition to alpha band activity, resting-state frontal theta power may also provide a marker for depression (Iosifescu, 2011;Bailey et al., 2018). Several studies have reported elevated frontal theta as a positive predictive factor for antidepressant response (Pizzagalli et al., 2002;Spronk et al., 2011;Rentzsch et al., 2014;Arns et al., 2015;Koo et al., 2017), although another study suggested that mood changes are negatively correlated with frontal theta activity (Knott et al., 2000). ...
... Resting-state EEG measures commonly associated with depression, such as frontal alpha asymmetry and frontal theta power, were not significantly altered by the treatment despite meaningful improvements in mood. These measures were hypothesised a priori to change with treatment because they had been linked to antidepressant response (Spronk et al., 2011;Arns et al., 2015;Arns et al., 2016). Recently, there have been some criticisms of the viability of alpha asymmetry as a relevant factor for depression (Van Der Vinne et al., 2017). ...
Transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, is a promising treatment for depression. Recent research suggests that tDCS efficacy can be augmented using concurrent cognitive emotional training (CET). However, the neurophysiological changes associated with this combined intervention remain to be elucidated. We therefore examined the effects of tDCS combined with CET using electroencephalography (EEG). A total of 20 participants with treatment resistant depression took part in this open-label study and received 18 sessions over 6 weeks of tDCS and concurrent CET. Resting-state and task-related EEG during a 3-back working memory task were aquired at baseline and immediately following the treatment course. Results showed an improvement in mood and working memory accuracy, but not response time, following the intervention. We did not find significant effects of the intervention on resting-state power spectral density (frontal theta and alpha asymmetry), time-frequency power (alpha event-related desynchronization and theta event-related synchronisation), or event-related potentials (P2 and P3 components). We therefore identified little evidence of neurophysiological changes associated with treatment using tDCS and concurrent CET, despite significant improvements in mood and near transfer effects of cognitive training to working memory accuracy. Further research incorporating a sham controlled group may be necessary to identify the neurophysiological effects of the intervention.
... Several studies have reported increased frontal theta power coupled to the non-response to antidepressants (Arns et al., 2012;Iosifescu et al., 2009;Knott et al., 1996), while others did not show any difference in the baseline theta power between responders and non-responders Cook et al., 2002). Another study found increased baseline theta at the frontal midline area (Fz electrode), associated with favourable symptom changes (Spronk et al., 2011). It is hypothesised (Olbrich and Arns, 2013;Olbrich et al., 2015) that theta in the frontal midline area probably reflects the activity of the anterior cingulate, whereas other studies (Arns et al., 2012;Cook et al., 2002;Cook et al., 1999;Iosifescu et al., 2009;Knott et al., 1996) have reported a more widespread area underlying frontal theta activity. ...
... We failed to replicate some previous reports on the association of theta and alpha power with treatment outcome (Bruder et al., 2008;Iosifescu et al., 2009;Jaworska et al., 2014;Pizzagalli et al., 2018;Spronk et al., 2011). As mentioned in the Introduction section, the results of previous studies on predictive efficacy of theta and alpha AUC-area under the curve of receiver operating characteristics, CI-confidence interval, NPV-negative predictive value, OAA1C-change of occipital alpha 1 asymmetry at week 1, OAA2C-change of occipital alpha 2 asymmetry at week 1, PFFC-change of prefrontal theta cordance at week 1, PFCC + OAA1C-change of prefrontal theta cordance at week 1 + change of occipital alpha 1 asymmetry at week 1, PPV-positive predictive value, SE-sensitivity, SPE-specificity, SSRI-selective serotonin reuptake inhibitor subgroup, SNRI-serotonin-norepinephrine reuptake inhibitor subgroup. ...
Background:
The study evaluated the effectiveness of EEG alpha 1, alpha 2 and theta power, along with prefrontal theta cordance (PFC), frontal and occipital alpha 1, alpha 2 asymmetry (FAA1/2, OAA1/2) at baseline and their changes at week 1 in predicting response to antidepressants.
Method:
Resting-state EEG data were recorded from 103 depressive patients that were treated in average for 5.1 ± 0.9 weeks with SSRIs (n = 57) and SNRIs (n = 46).
Results:
Fifty-five percent of patients (n = 56) responded to treatment (i.e.reduction of Montgomery-Åsberg Depression Rating Scale score ≥ 50%) and 45% (n = 47) of treated subjects did not reach positive treatment outcome. No differences in EEG baseline alpha and theta power or changes at week 1 for prefrontal, frontal, central, temporal and occipital regions were found between responders and non-responders. Both groups showed no differences at baseline PFC, FAA1/2 and OAA1/2 as well as change of FAA1/2 at week 1. The only parameters associated with treatment outcome were decrease of PFC in responders and increase of OAA1/2 at week 1 in non-responders. There was no influence of the used antidepressant classes on the results. The PFC change at week 1 (PFCC) (area under curve-AUC = 0.75) showed only a numerically higher predictive ability than OAA change in alpha 1 (OAA1C, AUC = 0.64)/alpha 2 (OAA2C, AUC = 0.63). A combined model, where OAA1C was added to PFCC (AUC = 0.79), did not significantly improve response prediction.
Conclusion:
Besides PFCC, we found that OAA1C/OAA2C might be another candidate for EEG predictors of antidepressant response.
... Recently, neurofeedback training (NFBT) has attracted the attention of researchers because it provides reliable information about brain function during rest, sensory stimulation, and cognitive tasks. It is a safe, inexpensive, and noninvasive method, making it a valuable technique for clinical and research purposes [19,20]. Despite the widespread use of neurofeedback to assess cortical changes in chronic musculoskeletal pain conditions, little is known about brain wave activity changes in CLBP patients. ...
Background Chronic non-specific low back pain (CNSLBP) is a public health issue associated with a complex interaction of biopsychosocial factors. Electroencephalography (neurofeedback) is one of the methods used to assess and treat psychological factors associated with pain and increase awareness of the activity of different parts of the brain. Objective The present clinical trial investigates the effectiveness of neurofeedback training (NFBT) exercises on psychological variables (pain, disability, and kensiophobia) in women with CNSLBP. Methods This was a clinical trial study. A total of 40 females with CNSLBP were recruited for the clinical trial. The patients were randomly divided into two groups, namely experimental and control (each group included 20 patients). The experimental group received NFBT for 8 weeks. Pain intensity, disability, and kinesiophobia were assessed via the visual analog scale, the Oswestry disability index, and the Tampa scale. Statistical analysis was performed using the SPSS software, version 26. The Shapiro-Wilk test was used to ensure the normality of the data distribution (P>0.05). The covariance test was used to compare results between groups. Results The results showed that the NFBT had a significant difference in reducing pain (P=0.000, ƞ2=0.693), disability (P=0.005, ηp2=0.253), and kinesiophobia (P=0.000, ηp2=0.904). Conclusion NFBT leads to a reduction in the perception of pain intensity, disability, and kinesiophobia; however, they do not address the underlying cause of pain in this group of patients. Instead, they only modulate the response to pain sensation processing.
... Using the auditory oddball ERP paradigm, Spronk and colleagues reported a decrease in N100 amplitude following rTMS treatment in MDD (Spronk et al., 2008). In a later study by the same group, again using the auditory oddball ERP paradigm, demonstrated a positive association between baseline N100 amplitude and antidepressant treatment outcome for MDD, suggesting that MDD patients with more negative N100 amplitudes at baseline will improve more on antidepressant medications (Spronk et al., 2011). In terms of TMS-EEG studies, in line with the results from the auditory oddball paradigm studies, Sun and colleagues demonstrated that a greater N100 amplitude at baseline led to a greater suicidality improvement after magnetic seizure therapy (MST) in MDD patients (Sun et al., 2016). ...
... The literature also encompasses a wide array of EEG features employed for the same purpose, such as power spectral features Al-Kaysi et al., 2017), coherence Mumtaz et al., 2017), mutual information (MI) , nonlinear features (Hasanzadeh et al., 2019), time-frequency processing , and wavelet coefficients (Mumtaz et al., 2017). Among these features, EEG power in different frequency bands and their combinations have received substantial attention in predicting MDD treatment (Wade et al., 2016;Arns et al., 2012;Bruder et al., 2008;Cook et al., 2002;Knott et al., 2000;Pellicciari et al., 2013;Spronk et al., 2011;Suffin and Emory, 1995;Tenke et al., 2011;Lebiecka et al., 2018). Notably, treatment response has been linked to cordance measures (Leuchter et al., 1994) and an Antidepressant Treatment Response (ATR) index (Iosifescu et al., 2009). ...
Background: Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR).
Methods: Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups – R and NR to rTMS treatment – we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field.
Results: We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28%, specificity at 94.23%, sensitivity reaching 96.29%, and precision standing at 94.54%, all achieved using SVM.
Conclusions: The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.
... 11 A previous study showed that increased frontal-central theta power was associated with favourable treatment outcomes in depression. 25 Another study found a strong positive correlation between ACC-generated theta activity in healthy people and activity in broad cortical regions, especially in the brain's right hemisphere, but no significant correlation in depressed patients. 9 However, unlike the findings of low ACC activity in patients with depression, increased pre-treatment ACC activity is a potential predictor of antidepressant efficacy. ...
Objective
Only about one-third of depressed patients respond to initial antidepressant treatment. Therefore, it is crucial to find effective predictors of antidepressants. The purpose of our study was to learn the relationship between EEG theta power, theta asymmetry, and the efficacy of escitalopram.
Methods
The study included 34 patients with depression. Before and after each patient’s course of treatment, EEG data was gathered. Both the Hamilton Anxiety Scale (HAMA) and the 17-item Hamilton Depression Scale (HAMD-17) were evaluated simultaneously. The natural logarithm of right frontal theta power minus left frontal theta power was used to calculate inter-electrode theta asymmetry (AT).
Results
First, our study found no statistically significant difference between intra-electrode theta power and inter-electrode AT before and after treatment (P ≥ 0.05). When we later looked at the data regarding treatment effects, the findings revealed that patients (n = 9) who did not respond to treatment had lower baseline theta power at C4 [6.190 (2.000, 12.990) vs 15.800 (7.255, 22.330), z = −2.166, P = 0.030]. The two groups had no difference in other electrodes (P ≥ 0.05). The AT of C3/C4 in non-responders (n = 9) was lower [0.012 (0.795) vs 0.733 (0.539), t = −3.224, P = 0.005]. However, there was no difference in inter-electrode AT between the two groups in F3/F4 and F7/F8 (P ≥ 0.05). We finally show that the theta power at C4 was negatively correlated with HAMD scores before treatment (r = −0.346, P = 0.045).
Conclusion
Our findings determined that increased theta power and positive asymmetry in the right frontal-central area correlate with favourable escitalopram treatment, providing a basis for finding predictive markers for antidepressants.
... In another 2011 study, a favorable association was found with the outcome of drug treatment for carriers of the Met/Met genotype [40]. However, another 2017 investigation indicated that there is a better therapeutic response to rTMS in patients with Val/Val homozygosity in the BDNF gene [41]. ...
Brain-derived neurotrophic factor (BDNF) has been studied as a biomarker of major depressive disorder (MDD). Besides diagnostic biomarkers, clinically useful biomarkers can inform response to treatment. We aimed to review all studies that sought to relate BDNF baseline levels, or BDNF polymorphisms, with response to treatment in MDD. In order to achieve this, we performed a systematic review of studies that explored the relation of BDNF with both pharmacological and non-pharmacological treatment. Finally, we reviewed the evidence that relates peripheral levels of BDNF and BDNF polymorphisms with the development and management of treatment-resistant depression.
... Analysis of variance was performed to test the differences in relative power (0.5-50 Hz frequencies) between responders and non-responders at baseline and week 2. The main effect of CBT Response (responder, nonresponder) and Time (baseline, week 2), and the interaction effect of CBT Response x Time were evaluated across 58 channels in sensor space and 148 regions of interest in source space. For post-hoc comparisons, when applicable, independent-sample t-statistics were used across all 58 channels and across frequency bands: delta band (0.5-4 Hz), theta band (4-8 Hz), alpha band (8-12 Hz), beta band (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma band . Cluster-based permutation testing 93 was applied separately within each frequency band to further correct for multiple comparisons. ...
Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5–4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8–12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.
... For example, early changes in facial emotion recognition has been identified as a predictor of response to antidepressants in a systematic review (Park et al., 2018). Furthermore, performance level in a verbal memory task has also been proposed as a cognitive marker (Spronk et al., 2011). However, the results are heterogeneous and some studies did not report a significant relationship between cognitive functions and treatment response (Groot et al., 1996;Doraiswamy et al., 2003;Alexopoulos et al., 2007;Lin et al., 2014;Bingham et al., 2015), and others did (Potter et al., 2004;Story et al., 2008;Bruder et al., 2014). ...
Background
Cognitive impairments are prevalent in patients with unipolar and bipolar depressive disorder (UDD and BDD, respectively). Considering the fact assessing cognitive functions is increasingly feasible for clinicians and researchers, targeting these problems in treatment and using them at baseline as predictors of response to treatment can be very informative.
Method
In a naturalistic, retrospective study, data from 120 patients (Mean age: 33.58) with UDD ( n = 56) and BDD ( n = 64) were analyzed. Patients received 20 sessions of bilateral rTMS (10 Hz over LDLPFC and 1 HZ over RDLPFC) and were assessed regarding their depressive symptoms, sustained attention, working memory, and executive functions, using the Beck Depression Inventory (BDI-II) and Neuropsychological Test Automated Battery Cambridge, at baseline and after the end of rTMS treatment course. Generalized estimating equations (GEE) and logistic regression were used as the main statistical methods to test the hypotheses.
Results
Fifty-three percentage of all patients ( n = 64) responded to treatment. In particular, 53.1% of UDD patients ( n = 34) and 46.9% of BDD patients ( n = 30) responded to treatment. Bilateral rTMS improved all cognitive functions (attention, working memory, and executive function) except for visual memory and resulted in more modulations in the working memory of UDD compared to BDD patients. More improvements in working memory were observed in responded patients and visual memory, age, and sex were determined as treatment response predictors. Working memory, visual memory, and age were identified as treatment response predictors in BDD and UDD patients, respectively.
Conclusion
Bilateral rTMS improved cold cognition and depressive symptoms in UDD and BDD patients, possibly by altering cognitive control mechanisms (top-down), and processing negative emotional bias.
... The EEG is particularly useful as a result of its high temporal resolution, low cost, portable device, which enables patients to be in any position required for the assessment, and is not restricted by metallic implants in the body or claustrophobia (29,96,97). However, the high temporal resolution comes at a cost of low accuracy concerning structural identification in deep brain structures, in particular, but also in the brain, in general (97,98). ...
Background:
Whiplash injuries typically occur from a motor vehicle collision and lead to chronic whiplash-associated disorders (CWAD) in 20% to 50% of cases. Changes in neurotransmission, metabolism, and networks seem to play a role in the pathogenic mechanism of CWAD.
Objectives:
To further elucidate the functional brain alterations, a neurophysiological study was performed to investigate the somatosensory processing of CWAD patients by comparing the event-related potentials (ERPs) resulting from electrical nociceptive stimulation between patients suffering from CWAD and healthy controls (HC).
Study design:
Case-control study.
Setting:
University Hospital in Ghent.
Methods:
In this case-control study (CWAD patients/HC: 50/50), ankle and wrist electrical pain thresholds (EPT), and amplitude and latency of the event-related potentials (ERPs) resulting from 20 electrical stimuli were investigated. Correlations between the ERP characteristics, EPT, self-reported pain, disability, pain catastrophizing, and self-reported symptoms of central sensitization were investigated.
Results:
Only the latency of the P3 component after left wrist stimulation (t = -2.283; P = 0.023) differed between both groups. In CWAD patients, the ankle EPT correlated with the amplitude of the corresponding P1 (rho s = 0.293; P = 0.044) and P3 (rho s = 0.306; P = 0.033), as well as with the amplitude of the P3 to left wrist stimulation (rho s = 0.343; P = 0.017). Self-reported symptoms of CS correlated with right wrist P3 amplitude (rho s = 0.308; P = 0.030) and latency (rho s = -0.341; P = 0.015), and the worst pain reported during the past week was correlated with left wrist P1 latency (rho s = 0.319; P = 0.029).
Limitations:
Although the inclusion criteria stated that CWAD patients had to report a moderate-to-severe pain-related disability, 8 of the included CWAD patients (that scored above this threshold in the inclusion questionnaire), scored below the required cutoff at baseline.
Conclusions:
The CWAD patients did not show signs of hypersensitivity, but their ERP characteristics were related to the intensity of the applied stimulus, self-reported symptoms of CS, and the worst pain reported during the past week.
... Hence, effective treatment of depression is a major public health challenge (Sartorius, 2001). Nowadays, compared to some commonly available treatments, such as cognitive behavioral therapy (Siegle et al., 2006;Ritchey et al., 2011) and antidepressant medication (Spronk et al., 2011), electroconvulsive therapy (ECT) (Bouckaert et al., 2016;Yrondi et al., 2018;Takamiya et al., 2019) has long been considered to be the most effective and rapid treatment for depression, and around 70-90% of the depressed patients showed an improvement when treated with ECT (Kellner et al., 2014). However, the mechanisms underlying the action of ECT are still not known. ...
Objectives
Several studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated.
Methods
Nineteen-channel resting-state eyes-closed EEG signals were collected from 24 MDD patients pre- and post-ECT treatment. Functional brain networks were constructed by using various coupling methods and binarization techniques. Changes in functional connectivity and network metrics after ECT treatment and relationships between network metrics and clinical symptoms were explored.
Results
ECT significantly increased global efficiency, edge betweenness centrality, local efficiency, and mean degree of alpha band after ECT treatment, and an increase in these network metrics had significant correlations with decreased depressive symptoms in repeated measures correlation. In addition, ECT regulated the distribution of hubs in frontal and occipital lobes.
Conclusion
ECT modulated the brain’s global and local information-processing patterns. In addition, an ECT-induced increase in network metrics was associated with clinical remission.
Significance
These findings might present the evidence for us to understand how ECT regulated the topology organization in functional brain networks of clinically remitted depressive patients.
... In this review, we will focus on EEG as it stands out as a valuable noninvasive tool that provides relevant information of the brain function during rest, sensory stimulation or execution of cognitive tasks (Spronk et al., 2011). Additionally, EEG has a much simpler methodology and lower cost, but EEG signals in their raw state do not serve for clinical interpretation, given the overlapped neuronal activity from different sources. ...
The management of chronic neuropathic pain remains a challenge, because pain is subjective, and measuring it
objectively is usually out of question. However, neuropathic pain is also a signal provided by maladaptive
neuronal activity. Thus, the integral management of chronic neuropathic pain should not only rely on the
subjective perception of the patient, but also on objective data that measures the evolution of neuronal activity.
We will discuss different objective and subjective methods for the characterization of neuropathic pain. Additionally,
the gaps and proposals for an integral management of chronic neuropathic pain will also be discussed.
The current management that relies mostly on subjective measures has not been sufficient, therefore, this has
hindered advances in pain management and clinical trials. If an integral characterization is achieved, clinical
management and stratification for clinical trials could be based on both questionnaires and neuronal activity.
Appropriate characterization may lead to an increased effectiveness for new therapies, and a better quality of life
for neuropathic pain sufferers.
... Findings with baseline and change in theta oscillations have been observed with traditional medications for depression, though conflicting results have been found (Baskaran et al., 2018;Iosifescu et al., 2009;Knott et al., 2000;Knott et al., 1996;Spronk et al., 2011;Tenke et al., 2011). With ketamine, the findings of were increase in frontal theta power, with an observation of a trend towards increased frontal theta power relating to a larger decrease in MADRS score at one day post-infusion. ...
Background
Sub-anesthetic ketamine doses rapidly reduce depressive symptoms, although additional investigations of the underlying neural mechanisms and the prediction of response outcomes are needed. Electroencephalographic (EEG)-derived measures have shown promise in predicting antidepressant response to a variety of treatments, and are sensitive to ketamine administration. This study examined their utility in characterizing changes in depressive symptoms following single and repeated ketamine infusions.
Methods
Recordings were obtained from patients with treatment-resistant major depressive disorder (MDD) (N = 24) enrolled in a multi-phase clinical ketamine trial. During the randomized, double-blind, crossover phase (Phase 1), patients received intravenous ketamine (0.5 mg/kg) and midazolam (30 μg/kg), about 1 week apart. For each medication, three resting, eyes-closed recordings were obtained per session (pre-infusion, immediately post-infusion, 2 h post-infusion), and changes in power (delta, theta1/2/total, alpha1/2/total, beta, gamma), alpha asymmetry, theta cordance, and theta source-localized anterior cingulate cortex activity were quantified. The relationships between ketamine-induced changes with early (Phase 1) and sustained (Phases 2,3: open-label repeated infusions) decreases in depressive symptoms (Montgomery-Åsberg Depression Rating Score, MADRS) and suicidal ideation (MADRS item 10) were examined.
Results
Both medications decreased alpha and theta immediately post-infusion, however, only midazolam increased delta (post-infusion), and only ketamine increased gamma (immediately post- and 2 h post-infusion). Regional- and frequency-specific ketamine-induced EEG changes were related to and predictive of decreases in depressive symptoms (theta, gamma) and suicidal ideation (alpha). Early and sustained treatment responders differed at baseline in surface-level and source-localized theta.
Conclusions
Ketamine exerts frequency-specific changes on EEG-derived measures, which are related to depressive symptom decreases in treatment-resistant MDD and provide information regarding early and sustained individual response to ketamine.
Clinical Trial Registration: ClinicalTrials.gov: Action of Ketamine in Treatment-Resistant Depression, NCT01945047
... These findings go along with other findings indicating that global alpha and theta rhythms functionally interact during both relaxation and attentional tasks (Klimesch, 1999;Buzsáki, 2006;Laufs et al., 2006). Furthermore, theta power has been used to predict response to depression treatment in several studies (Knott et al., 1996(Knott et al., , 2000Cook and Leuchter, 2001;Cook et al., 2002;Bares et al., 2008;Iosifescu et al., 2009;Spronk et al., 2011;Baskaran et al., 2012;Olbrich and Arns, 2013). Furthermore, theta power decreases while upper alpha power increases in several conditions (i.e., the early part of life until adulthood, in neurological disorders, and the transition phase from awake to sleeping), whereas the direction of their relationship is opposite for the late part of the lifespan (Klimesch, 1999). ...
Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22–80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8–13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8–10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.
... The N100 has often been implicated in studies on MDD and antidepressant response, for instance when recorded in an auditory oddball paradigm where larger N100 amplitudes (more negative) were found to be associated with better clinical response to several antidepressants (Spronk et al., 2011), and worse response to venlafaxine for males in the large iSPOT-D trial (van Dinteren et al., 2015). Furthermore, N100 is also an important component of the Loudness Dependent Auditory Evoked Potential (LDAEP) that has been shown to index serotonergic and possibly glutamatergic innervation (Kenemans and Kähkönen, 2010), and has received interest as a diagnostic and predictive biomarker in MDD. ...
... Recently, quantitative Electroencephalography (qEEG) has also been considered by researchers, as it provides reliable information about brain function during rest, sensory stimulation, and cognitive tasks. In addition, this method is safe, inexpensive, and non-invasive, making it a valuable apparatus for clinical practice and research applications (De Vries et al. 2013;Spronk, Arns, Barnett, 2011). Nevertheless, despite the wide applicability of qEEG for evaluation of cortical changes in chronic musculoskeletal conditions, relatively little knowledge exists on brain wave activity changes in CLBP patients. ...
Introduction
Research evidence indicates that maladaptive reorganization of the brain plays a critical role in amplifying pain experiences and pain chronification; however, no clear evidence of change exists in brain wave activity among patients with chronic low back pain (CLBP). The objective of this study was to assess brain wave activity in patients with CLBP, compared to healthy controls.
Methods
Twenty-five patients with CLBP and twenty-four healthy controls participated in the study. A quantitative electroencephalography device was used to assess brain wave activity in eyes-open and eyes-closed (EO and EC) conditions. The regional absolute and relative power of brain waves were compared between the groups.
Results
Our results showed a significant increase in the absolute power of theta (F=5.905, P=0.019), alpha (F=5.404, P=0.024) waves in patients with CLBP compared to healthy subjects in both EC and EO conditions. Patients with CLBP showed a reduced delta absolute power in the frontal region (F=5.852, P=0.019) and augmented delta absolute power in the central region (F=5.597, P=0.022) in the EO condition. An increased delta absolute power was observed in the frontal (F=7.563 P=0.008), central (F=10.430, P=0.002), and parietal (F=4.596, P=0.037) regions in patients with CLBP compared to the healthy subjects in the EC condition. In the EC condition, significant increases in theta relative power (F=4.680, P=0.036) in the parietal region were also found in patients with CLBP.
Conclusion
The increased absolute power of brain waves in people with CLBP may indicate cortical overactivity and changes in the pain processing mechanisms in these patients.
Highlights
Chronic low back pain (CLBP) increases the alpha, theta, and delta power in the brain.
CLBP is associated with increased brain wave activity in the frontal, central, and parietal regions.
Our findings suggest altered central pain processing in CLBP.
Plain Language Summary
Traditional diagnosis and treatment of CLBP are mainly focused on peripheral pathology. But, the modern neuroscience approach to pain highlights the role of cortical plasticity in chronic musculoskeletal pain. In this regard, several studies found structural and functional changes in the brain in patients with chronic pain. Detailed knowledge about cortical changes in CLBP can improve our understanding of mechanisms involved in CLBP, opening a new window to better treatment of LBP (Low back pain). This study investigated brain wave activity in patients with CLBP compared to healthy individuals. Our findings suggest increased brain activity in various parts of the brain in patients with chronic LBP. This finding indicates that CLBP treatment should focus on both peripheral and cortical factors rather than local tissue damage.
... Although some studies suggest that dysfunction in the rostral anterior cingulate cortex may contribute to the development of cognitive impairment, further research is needed to understand the precise relationship between impairments in cognitive function and MDD (66). In addition, the ability of antidepressant treatments to improve cognitive impairment in MDD and TRD patients is limited (67,68). Approximately 30% of patients successfully treated with SSRIs continue to report significant impairment in cognitive function (69). ...
... 22,23 A relationship between midline theta activity and antidepressant treatment response also exists, but with the same discrepant findings. 17,20,[24][25][26] Gamma-band deficits have also been implicated as a potential marker of MDD [27][28][29] and therapeutic antidepressant efficacy, [30][31][32] and increased gamma activity has been associated with the remission of depression symptoms. [30][31][32] Few studies have considered the role of sex in dysregulated network activity in MDD, and of those that exist, there appears to be no consensus. ...
Background:
Major depressive disorder is a chronic illness with a higher incidence in women. Dysregulated neural oscillatory activity is an emerging mechanism thought to underlie major depressive disorder, but whether sex differences in these rhythms contribute to the development of symptoms is unknown.
Methods:
We exposed male and female rats to chronic unpredictable stress and characterized them as stress-resilient or stress-susceptible based on behavioural output in the forced swim test and the sucrose preference test. To identify sex-specific neural oscillatory patterns associated with stress response, we recorded local field potentials from the prefrontal cortex, cingulate cortex, nucleus accumbens and dorsal hippocampus throughout stress exposure.
Results:
At baseline, female stress-resilient rats innately exhibited higher theta coherence in hippocampal connections compared with stress-susceptible female rats. Following stress exposure, additional oscillatory changes manifested: stress-resilient females were characterized by increased dorsal hippocampal theta power and cortical gamma power, and stress-resilient males were characterized by a widespread increase in high gamma coherence. In stress-susceptible animals, we observed a pattern of increased delta and reduced theta power; the changes were restricted to the cingulate cortex and dorsal hippocampus in males but occurred globally in females. Finally, stress exposure was accompanied by the time-dependent recruitment of specific neural pathways, which culminated in system-wide changes that temporally coincided with the onset of depression-like behaviour.
Limitations:
We could not establish causality between the electrophysiological changes and behaviours with the methodology we employed.
Conclusion:
Sex-specific neurophysiological patterns can function as early markers for stress vulnerability and the onset of depression-like behaviours in rats.
... Several pretreatment differences in electrophysiologic measures have been found among depressed patients that predict clinical response to antidepressant drugs [157][158][159][160][161]. Poor response to antidepressants treatment response has been associated with increased Theta and Delta power [153]. Higher pretreatment frontal Theta power was the best EEG predictor of decrease in depressive symptoms and also may serve as new biomarkers appropriate for the prediction of the antidepressant treatment outcome [162]. Pretreatment increased posterior alpha and theta in rostral anterior cingulate cortex prior to treatment, suggesting alpha or theta measures as probable predictors of clinical response to SSRI or other antidepressant drugs [158][159][160][161]. ...
Background
A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms.
Objective
Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for treatment of cognitive impairments.
Methods
This review includes description of brain oscillations in Alzheimer’s disease, bipolar disorder, attentiondeficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism.
Results
The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations.
Conclusion
In this article we examined nature of brain oscillations, association of brain rhythms with cognitive functions and relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.
... For example, parameters of EEG recorded during sleep have been found to be of discriminative and predictive value in MDD [4]. Another example are the parameters of the loudness dependency auditory evoked potential as predictors of antidepressant treatment outcome [5]. The absolute and relative power of alpha rhythms have been observed to be higher in MDD patients compared to healthy controls [6], whilst comparatively slower alpha rhythms have been linked to the ineffectiveness of treatment with certain antidepressants [7]. ...
... However, such changes to the P3a have also not been demonstrated in every study ( Kähkönen et al., 2007 ). Correlations of the mismatch response with depression severity have also not been consistent ( Chen et al., 2015 ;Qiu et al., 2011 ;Spronk et al., 2011 ). It is unclear exactly why the inconsistencies in the depression literature on the MMN and P3a response to deviant stimulus processing have occurred. ...
Major depressive disorder negatively impacts the sensitivity and adaptability of the brain's predictive coding framework. The current electroencephalography study into the antidepressant properties of ketamine investigated the downstream effects of ketamine on predictive coding and short-term plasticity in thirty patients with depression using the auditory roving mismatch negativity (rMMN). The rMMN paradigm was run 3–4 h after a single 0.44 mg/kg intravenous dose of ketamine or active placebo (remifentanil infused to a target plasma concentration of 1.7 ng/mL) in order to measure the neural effects of ketamine in the period when an improvement in depressive symptoms emerges. Depression symptomatology was measured using the Montgomery-Asberg Depression Rating Scale (MADRS); 70% of patients demonstrated at least a 50% reduction their MADRS global score. Ketamine significantly increased the MMN and P3a event related potentials, directly contrasting literature demonstrating ketamine's acute attenuation of the MMN. This effect was only reliable when all repetitions of the post-deviant tone were used. Dynamic causal modelling showed greater modulation of forward connectivity in response to a deviant tone between right primary auditory cortex and right inferior temporal cortex, which significantly correlated with antidepressant response to ketamine at 24 h. This is consistent with the hypothesis that ketamine increases sensitivity to unexpected sensory input and restores deficits in sensitivity to prediction error that are hypothesised to underlie depression. However, the lack of repetition suppression evident in the MMN evoked data compared to studies of healthy adults suggests that, at least within the short term, ketamine does not improve deficits in adaptive internal model calibration.
... Karalis e col (2016) Na clínica um dos principais marcadores eletrofisiológicos da depressão são as alterações nas atividades das bandas delta, teta e alfa, o que se correlaciona com os nossos achados no CPF e na ABL (tabela 1). Em humanos a maior atividade em teta no córtex frontal é associada à resposta de antidepressivos (KNOTT et al., 1996;SPRONK et al., 2011). Assim como o aumento da atividade em alfa no córtex frontal é associada à predisposição da resposta ao tratamento (BRUDER et al., 2008). ...
... New methods in neuroscience such as deep learning, could assist in this quest (Tjepkema-Cloostermans et al., 2018) as well as using a multimodal and integrative approach where data from various domains is combined in order to optimize prediction, including cognitive, psychological and genetic information (e.g. Spronk et al., 2011). For future similar studies, we propose that records are interpreted by two independent EEG specialists, both blinded to group and treatment. ...
Background:
MDD patients with abnormal EEG patterns seem more likely to be non-responsive to the antidepressants escitalopram and venlafaxine, but not sertraline, than patients without EEG abnormalities. This finding suggests that patients with both MDD and abnormal EEGs may differentially respond to antidepressant treatment. In the current study, we investigated whether depressed patients with an abnormal EEG show a normalization of the EEG related to antidepressant treatment and response and whether such effect is drug specific, and whether having had early life stress (ELS) increases the chance of abnormal activity.
Methods:
Baseline and week 8 EEGs and depression symptoms were extracted from a large multicenter study (iSPOT-D, n = 1008) where depressed patients were randomized to escitalopram, sertraline, or venlafaxine-XR treatment. We calculated Odds Ratios of EEG normalization and depression response in patients with an abnormal EEG at baseline, comparing sertraline versus other antidepressants.
Results:
Fifty seven patients with abnormal EEGs were included. EEGs did not normalize significantly more with sertraline compared to other antidepressants (OR = 1.9, p = .280). However, patients with a normalized EEG taking sertraline were 5.2 times more likely to respond than subjects taking other antidepressants (p = .019). ELS was not significantly related to abnormal activity.
Limitations:
Neurophysiological recordings were limited in time (two times 2-minute EEGs) and statistical power (n = 57 abnormal EEGs).
Conclusions:
Response rates in patients with normalized EEG taking sertraline were significantly larger than in subjects treated with escitalopram/venlafaxine. This adds to personalized medicine and suggests a possible drug repurposing for sertraline.
... Thus, it is not surprising that the normalization of theta activity within the anterior cingulate cortex (ACC) is a reportedly reliable measure of treatment response [90,91], as well as reduced theta cordance in the PFC [92,93]. Furthermore, excessive baseline theta oscillations in the frontal-midline regions of the brain were predictive of a successful treatment response and improved verbal memory post-treatment [94]. ...
Major depressive disorder (MDD) is a debilitating chronic illness that is two times more prevalent in women than in men. The mechanisms associated with the increased female susceptibility to depression remain poorly characterized. Aberrant neuronal oscillatory activity within the putative depression network is an emerging mechanism underlying MDD. However, innate sex differences in network activity and its contribution to depression vulnerability have not been well described. In this review, current evidence of sex differences in neuronal oscillatory activity, including the influence of sex hormones and female cycling, will first be described followed by evidence of disrupted neuronal circuit function in MDD and the effects of antidepressant treatment. Lastly, current knowledge of sex differences in MDD-associated aberrant circuit function and oscillatory activity will be highlighted, with an emphasis on the role of sex steroids and female cycling. Collectively, it is clear that there are significant gaps in the literature regarding innate and pathologically associated sex differences in network activity and that the elucidation of these differences is invaluable to our understanding of sex-specific vulnerabilities and therapies for MDD.
... While left versus right hemispheric activation has been associated with treatment responses, a body of literature has raised the possibility of hemispheric asymmetry in the alpha band [11,18,20,21]. Similarly, conflicting studies have investigated theta activity and reported that increased absolute theta power [22] or decreased relative theta power [17] is associated with the treatment response. To determine the emergent effectiveness of antidepressant therapy, theta cordance [23] has been derived from both absolute and relative theta power. ...
This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited 55 outpatients with TRD who were randomized into three approximately equal- sized groups (A: 0.5 mg/kg ketamine; B: 0.2 mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton Depression Rating Scale (HDRS) scores. At baseline, responders showed a significantly weaker EEG theta power than did non- responders (p < 0.05). Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and theta cordance at post-treatment than at baseline (p < 0.05). Furthermore, our baseline EEG predictor classified responders and non-responders with 81.3
9.5% accuracy, 82.1
8.6% sensitivity and 91.9
7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry and cordance at baseline and early post-treatment changes. The prefrontal EEG patterns at baseline may account for recognizing ketamine effects in advance. Our randomized, double- blind, placebo-controlled study provides information regarding clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.
Introduction
Diagnostic and treatment accuracy of depression can lead to a better and possibly earlier response and remission in patients. The literature, though scanty, seems to suggest that quantitative electroencephalography (QEEG) can predict the outcome of antidepressant effects.
Methodology
Articles published between January 1990 and July 2019, including those dealing with QEEG recordings before and after the initiation of antidepressant medication, were included. The pooled effect size and subgroup analysis of waveforms were calculated to predict response to antidepressants.
Result
In all, 572 results were retrieved from the searches, of which 20 studies were included. Pooled data using a random-effects model (REM) calculated an effect size of 0.80 (95% CI [0.64–0.97]). Heterogeneity of the sample was low with Tau² = 0.02; df = 18 ( P = .30); I² = 12%. Moreover, subgroup analysis showed that theta band frequencies were better at predicting response than alpha band frequencies (the standard mean difference [SMD] for theta was 0.91 compared to 0.68 for alpha waves).
Conclusions
QEEG is a valuable predictor of the antidepressant response. Among the EEG frequencies, the theta band showed the most significant change with treatment.
Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.
The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.
Introduction:
Asymmetrical alpha and frontal theta activity have been discussed as neurobiological markers for antidepressant treatment response. While most studies focus on resting-state EEG, there is evidence that task-related activity assessed at multiple time points might be superior in detecting subtle early differences.
Methods:
This was a naturalistic study design assessing participants in a psychiatric in- and outpatient hospital setting. We investigated stimulus-related EEG asymmetry (frontal and occipital alpha-1 and alpha-2) and power (frontal midline theta) assessed at baseline and 1 week after initiation of pharmacological depression treatment while presenting affective stimuli. We then compared week 4 responders and nonresponders to antidepressant treatment.
Results:
Follow-up analyses of a significant group × emotion × time interaction (p < 0.04) for alpha-1 asymmetry showed that responders differed significantly at baseline in their asymmetry scores in response to sad compared to happy faces with a change in this pattern 1 week later. Nonresponders did not show this pattern. No significant results were found for alpha-2, occipital alpha-1, and occipital alpha-2 asymmetry or frontal midline theta power.
Discussion:
Our study addresses the gap in comparisons of task-related EEG activity changes measured at two time points and supports the potential value of this approach in detecting early differences in responders versus nonresponders to pharmacological treatment. Important limitations include the small sample size and the noncontrolled study design.
Objective:
To evaluate the association between sleep-wake rhythm and cardiometabolic parameters.
Material and methods:
103 participants, aged 25-64 years, underwent actigraphy study with Actigraph Actilife GT3X + device (USA) for 7 days. We assessed actigraphy indicators (physical activity and sleep data), anthropometric indicators, blood pressure and laboratory parameters. Actigraphy data was processed using the nparACT package in the R program with the calculation of nonparametric indicators: intraday variability, interday stability, the average level of lowest activity for five hours (L5) and ten hours with the highest activity (M10), relative amplitude is the ratio of M10/L5.
Results:
The nonparametric analysis showed an association of the higher night activity with sleep effectiveness, wake after sleep onset, indicators of physical activity. A more stable sleep pattern is associated with more steps, less weight and waist circumference, lower levels of diastolic blood pressure, creatinine and insulin. Increased fragmentation of sleep patterns is associated with increased CRP and increased sedentary time. Participants with higher activity contrast have less waist circumference, hips and body mass index, lower levels of CRP and insulin.
Conclusions:
Rhythm and quality of sleep are important parameters associated with cardiometabolic indicators. Stable sleep patterns, higher activity during the day and lower night activity are associated with a more favorable condition of cardiovascular system.
The literature review provides data on one of the types of biomarkers - EEG predictors of the therapeutic response of patients with different types of mental pathology. It has been shown that the quantitative parameters of the electroencephalogram (EEG) recorded before the start of the treatment course reflect not only the current functional state of the patient's brain, but also its adaptive resources in terms of the possibility and magnitude of response to therapy. The identified EEG predictors of the therapeutic response in patients with depression, schizophrenia and some other mental disorders have a sufficiently high prognostic ability, sensitivity and specificity in determining responders and non-responders, make it possible to carry out a quantitative prediction of the patient's condition after a course of treatment, and also to assist the clinician in choosing medications for optimal therapy.
Mood disorders such as depression and bipolar disorder are common mental illnesses, affecting millions of patients worldwide. The application of newly available brain imaging methods to the study of mood disorders holds substantial promise in uncovering the brain mechanisms affected in these illnesses. This comprehensive and authoritative text features contributions from leading international experts, providing easily accessible information on the study of the brain mechanisms involved in the causation of mood disorders and the available treatments. Topics covered include the potential of magnetoencephalography (MEG), neuroimaging brain inflammation in depression, electrophysiology studies in mood disorders, and the applications of machine learning, filling an important gap in available neuropsychiatric literature and highlighting new developments. An invaluable resource for practitioners in the fields of psychiatry, neurology, primary care medicine, and related mental health professions, as well as researchers, students, graduate and post-graduate trainees.
Introduction
Many commonly prescribed drugs cause cognitive deficits. We investigated whether parameters of the resting-state electroencephalogram (rsEEG) are related to the severity of cognitive impairments associated with administration of the antiseizure drug topiramate (TPM) and the benzodiazepine lorazepam (LZP).
Methods
We conducted a double-blind, randomized, placebo-controlled crossover study. After a baseline visit, subjects completed three sessions at which they received either a single dose of TPM, LZP, or placebo. Four-hours after drug administration and at baseline, subjects completed a working memory (WM) task after their rsEEG was recorded. After quantifying drug-related behavioral (WM accuracy (ACC)/reaction time (RT)) and electrophysiological (alpha, theta, beta (1,2), gamma power) change for each subject, we constructed drug-specific mixed effects models of change for each WM and EEG measure. Regression models were constructed to characterize the relationship between baseline rsEEG measures and drug-related performance changes.
Results
Linear mixed effects models showed theta power increases in response to TPM administration. The results of the regression models revealed a number of robust relationships between baseline rsEEG parameters and TPM-related, but not LZP-related, WM impairment.
Conclusions
We showed for the first time that parameters of the rsEEG are associated with the severity of TPM-related WM deficits; this suggests that rsEEG measures may have novel clinical applications in the future.
Objective
Previous studies have demonstrated the efficacy of reduction of prefrontal theta cordance (RC) after 1week of treatment in the prediction of antidepressant response. Our study aimed to compare the ability of RC in the prediction of response to various treatments.
Methods
In total, 167 inpatients with MDD were treated with various antidepressants and low-frequency rTMS for 4 weeks. The primary efficacy measure was MADRS score, assessed at baseline, weeks − 1, − 2, and at the end of study. The EEG was recorded at baseline and after 1 week. Prefrontal theta cordance was calculated as an average from Fp1, Fp2 and Fz electrodes.
Results
Logistic regression identified RC as a predictor of response to SSRI, SNRI, NDRI and rTMS but not for NaSSA. Predictive parameters of RC for response to mentioned antidepressant classes were as follows: For SSRI (N = 58), the AUC of ROC analysis yielded value of 0.77, positive predictive value (PPV) of RC at week 1 was 0.81 and negative predictive values (NPV) of RC at week 1 was 0.73. For SNRI (N = 47), the AUC of ROC analysis yielded value of 0.77, PPV of RC at week 1 was 0.72 and NPV of RC at week 1 was 0.84. For NDRI (N = 22), the AUC of ROC analysis yielded value of 0.87, PPV of RC at week 1 was 0.91 and NPV of RC at week 1 was 0.82. For rTMS (N = 25), the AUC of ROC analysis yielded value of 0.75, PPV of RC at week 1 was 0.6 and NPV of RC at week 1 was 1.0. AUC of ROC analysis of RC were not significantly different among antidepressants.
Conclusion
Prefrontal QEEG cordance is a promising tool predicting the response to various antidepressive interventions. In this study, the predictive efficacy of 1-week reduction of QEEG prefrontal theta cordance for response to SSRI, SNRI, NDRI and rTMS was comparable.
Background:
Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we proposed a method based on machine learning to classify responders (R) and non- responders (NR) to rTMS treatment for major depression disorder (MDD) patients.
Methods:
19 electrodes resting state EEG was recorded from 46 MDD patients before treatment. Then patients underwent 7 weeks of rTMS, and 23 of them responded to treatment. Features extracted from EEG include Lempel-Ziv complexity (LZC), Katz fractal dimension (KFD), correlation dimension (CD), the power spectral density, features based on bispectrum, frontal and prefrontal cordance and combination of them. The most relevant features were selected by the minimal-redundancy-maximal-relevance (mRMR) feature selection algorithm. For classifying two groups of R and NR, k-nearest neighbors (KNN) were applied. The performance of the proposed method was evaluated by leave-1-out cross-validation. For further study, the capability of features in differentiating R and NR was investigated by a statistical test.
Results:
Effective EEG features for prediction of rTMS treatment response were found. EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands and CD were the most discriminative features. Power of beta classified R and NR with the high performance of 91.3% accuracy, 91.3% specificity, and 91.3% sensitivity.
Limitations:
Lack of large sample size restricted our method for using in clinical applications.
Conclusion:
This considerable high accuracy indicates that our proposed method with power and some of the nonlinear and bispectral features can lead to promising results in predicting treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording.
The constantly growing contribution of depressive disorders to the global disease statistics calls for a growth of treatment effectiveness and optimization. Antidepressants are the most frequently prescribed medicines for depressive disorders. However, development of a standardized pharmacotherapeutic approach is burdened by the genomic heterogeneity, lack of reliable predictive biomarkers and variability of the medicines metabolism aggravated by multiple side effects of antidepressants. According to modern assessments up to 20 % of the genes expressed in our brain are involved in the pathogenesis of depression. Large-scale genetic and genomic research has found a number of potentially prognostic genes. It has also been proven that the effectiveness and tolerability of antidepressants directly depend on the variable activity of the enzymes that metabolize medicines. Almost all modern antidepressants are metabolized by the cytochrome P450 family enzymes. The most promising direction of research today is the GWAS (Genome-Wide Association Study) method that is aimed to link genomic variations with phenotypical manifestations. In this type of research genomes of depressive patients with different phenotypes are compared to the genomes of the control group containing same age, sex and other parameters healthy people. Notably, regardless of the large cohorts of patients analyzed, none of the GWA studies conducted so far can reliably reproduce the results of other analogous studies. The explicit heterogeneity of the genes associated with the depression pathogenesis and their pleiotropic effects are strongly influenced by environmental factors. This may explain the difficulty of obtaining clear and reproducible results. However, despite any negative circumstances, the active multidirectional research conducted today, raises the hope of clinicians and their patients to get a whole number of schedules how to achieve remission faster and with guaranteed results
Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge.
Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features.
Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 “concentration difficulty” scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1.
Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, “biomarker”-based treatment approaches.
Introduction: Vortioxetine is a novel antidepressant drug approved for the treatment of major depressive disorder (MDD) in adults. It is formulated into tablets and has a dose range of 5–20 mg. The recommended starting dose is 10 mg administered orally once daily without the need for food.
Areas covered: This review focuses on the preclinical and clinical discovery of vortioxetine. It analyzes the pharmacological, neurochemical, and behavioral mechanisms of the medication and how these contribute to its potential therapeutic advantages as described in published preclinical and clinical studies and product labels.
Expert opinion: Vortioxetine displays high affinity for serotonin transporter (SERT), and serotonin 5-HT3, 5HT1A, 5HT7 receptors. Functional studies show that vortioxetine acts as a SERT blocker, a 5-HT3, 5-HT7 receptor antagonist, and a 5-HT1A receptor agonist. The drug is active in animal models predictive of antipsychotic and antidepressant activities and demonstrates procognitive effects in several animal models that assessed memory, cognition, and executive functions. Short- and long-term clinical trials demonstrated the clinical efficacy of vortioxetine in treating depressive symptoms and cognitive deficits in MDD patients. It also displays fairly benign safety and tolerability profiles. Vortioxetine’s unique psychopharmacological properties might contribute to an improved clinical outcome in MDD patient populations.
Представлены результаты анализа опубликованных за рубежом психофизиологических исследований, посвященных проблеме использования электроэнцефалографических (ЭЭГ) показателей в качестве объективных и надежных биомаркеров стресса, экспериментально вызванного у испытуемых в лабораторных условиях. Описаны основные экспериментальные протоколы, используемые для индуцирования стресса у здоровых испытуемых. На основании анализа опубликованных работ выделены и детально описаны основные электроэнцефалографические биомаркеры стресса, представлены возможные перспективы использования данных маркеров в клинической практике для диагностики психических расстройств и формирования группы мишеней для исследования в условиях экспериментального воздействия и терапии. Проанализированы принципиальные ограничения использования электроэнцефалографических биомаркеров в качестве основного диагностического инструмента в фундаментальных научных и прикладных исследованиях. Дано представление о перспективах дальнейшего использования данных ЭЭГ для изучения феномена стресса. Показано, что ЭЭГ-и биомаркеры вызванных потенциалов (ВП-биомаркеры) наряду с клиническими (нейроэндокринными, иммунными и т.п.) биомаркерами и биомаркерами, полученными при использовании других методов нейровизуализации (позитронно-эмиссионной томографии, ПЭТ и функциональной магнитно-резонансной томографии, фМРТ), являются информативным инструментом диагностики стресса и его последствий.
Ключевые слова: стресс, электроэнцефалография, биомаркеры
Studies suggest that neuropsychological measures may provide prognostic information regarding SSRI treatment response, yet it is unclear which specific cognitive domains are the most effectual predictors. The aim of this study was to characterize the cognitive profile associated with SSRI nonresponse using a comprehensive set of neuropsychological tests. Participants (N = 32) met criteria for current major depressive episode. Assessment followed pre-treatment medication washout. Clinical response was measured after 3-month open-label SSRI treatment. Groups did not differ by demographic characteristics, intelligence or depression severity. Responders outperformed nonresponders across all cognitive domains, with the largest differences observed in executive, language and working memory functions. Results indicate poorer global cognitive functioning is predictive of treatment nonresponse. Deficits were most pronounced in tests demanding greater mental search and manipulation rather than speeded motor output. Cognitive slowing may mediate the working memory and executive function deficits found in nonresponders. These findings can inform exploration for pharmacogenetic endophenotypes.
Antidepressants are important in the treatment of depression, and selective serotonin reuptake inhibitors are first-line pharmacologic options. However, only 50% to 70% of patients respond to first treatment and <40% remit. Since depression is associated with substantial morbidity, mortality, and family burden, it is unfortunate and demanding on health resources that patients must remain on their prescribed medications for at least 4 weeks without knowing whether the particular antidepressant will be effective. Studies have suggested a number of predictors of treatment response, including clinical, psychophysiological, neuroimaging, and genetics, each with varying degrees of success and nearly all with poor prognostic sensitivity and specificity. Studies are yet to be conducted that use multiple measures from these different domains to determine whether sensitivity and specificity can be improved to predict individual treatment response. It is proposed that a focus on standardized testing methodologies across multiple testing modalities and their integration will be crucial for translation of research findings into clinical practice.
The noradrenergic and dopaminergic systems are targets for antidepressants and are stimulated by serotonergic antidepressant drugs. The COMT enzyme inactivates catecholamines, and the COMT Val(108/158)Met polymorphism (rs4680) influences the enzyme activity. Clinical studies on the effect of rs4680 on antidepressant response gave contrasting results.
We studied the effect of rs4680 on response to paroxetine antidepressant monotherapy at doses administered upon clinical need.
Fifty-five consecutively referred outpatients affected by a major depressive episode without psychotic features in course of major depressive disorder were administered paroxetine at a mean daily dose of 31.64 mg for 1 month. Changes in severity of depression were assessed with weekly Hamilton depression ratings and analyzed with repeated measures analysis of variance in the context of general linear model, taking into account potential confounding variables (age, sex, number of previous illness episodes, duration of current episode and paroxetine daily dose).
rs4680 significantly interacted with time in affecting antidepressant response to paroxetine, with outcome being inversely proportional to the enzyme activity: better effects in Met/Met homozygotes, worse effects in Val/Val homozygotes and intermediate effects in heterozygotes. The effect became significant at the third week of treatment. Paroxetine daily dose was proportional to baseline severity, but did not influence outcome.
This is the first study that reports a positive effect of rs4680 on response to selective serotonin reuptake inhibitors monotherapy in a Caucasian sample. Our findings support the hypothesis that factors affecting catecholaminergic neurotransmission might contribute to shape the individual response to antidepressants.
This study demonstrates that the EEG phenotypes as described by Johnstone, Gunkelman & Lunt are identifiable EEG patterns with good inter-rater reliability. Furthermore, it was also demonstrated that these EEG phenotypes occurred in both ADHD subjects as well as healthy control subjects. The Frontal Slow and Slowed Alpha Peak Frequency and the Low Voltage EEG phenotype discriminated ADHD subjects best from controls (however the difference was not significant). The Frontal Slow group responded to a stimulant with a clinically relevant decreased number of false negative errors on the CPT. The Frontal Slow and Slowed Alpha Peak Frequency phenotypes have different etiologies as evidenced by the treatment response to stimulants. In previous research Slowed Alpha Peak Frequency has most likely erroneously shown up as a frontal theta sub-group. This implies that future research employing EEG measures in ADHD should avoid using traditional frequency bands, but dissociate Slowed Alpha Peak Frequency from frontal theta by taking the individual alpha peak frequency into account. Furthermore, the divergence from normal of the frequency bands pertaining to the various phenotypes is greater in the clinical group than in the controls. Investigating EEG phenotypes provides a promising new way to approach EEG data, explaining much of the variance in EEGs and thereby potentially leading to more specific prospective treatment outcomes.
Repetitive transcranial magnetic stimulation (rTMS) treatment for depression has been under investigation in many controlled studies over the last 20 years. Little is known about the neurobiological action of rTMS in patients. We therefore investigated pre- and post-treatment effects on QEEG, ERP's and behavior (BDI and NEO-FFI).
rTMS treatment was applied in 8 subjects for an average of 21 sessions to the left Dorsolateral Prefrontal Cortex (left DLPFC). Clients were assessed on a QEEG and Oddball ERP evaluation pre- and post-treatment. Clients were stimulated over the left DLPFC with 10 Hz rTMS (100% MT). Furthermore, rTMS treatment was complimented by psychotherapy.
All subjects showed full remission within 20 sessions and there was a significant reduction in depressive symptomatology (BDI score) after 10 and 15 sessions and a clear decrease in the Neuroticism and an increase on the extraversion scale of the NEO-FFI personality questionnaire. Pre- and post-QEEG measurements did not reveal treatment specific effects, but only an indirect right frontal increase in delta power. On the other hand, ERP measures did reveal treatment specific effects by showing an increased positivity in the post-treatment ERP's specifically left frontal. The P2 amplitude demonstrated a significant left frontal increase in amplitude, whereas for the negative N1 and N2 a significant decrease in amplitude was observed.
The results of this pilot study demonstrate that rTMS can be a safe and efficacious treatment modality for depression. Furthermore, a specific left frontal increase in positivity for the ERP's was found (increased P2 and decreased N1 and N2 components) most likely related to the rTMS over the left DLPFC. Furthermore, there was no change in the alpha asymmetry lending support to the fact that frontal alpha asymmetry can be considered a trait marker for depression. The findings from this pilot study require future replication with larger sample sizes.
We measured event-related potential (ERP) component amplitudes to four intensities of randomly presented tones. Patients diagnosed with major depressive disorder were tested prior to and following a clinical trial of antidepressant medication. Slope of P2 amplitude as a function of stimulus intensity was calculated for each subject and condition. Subjects were divided into two groups (responders and nonresponders) based on their Hamilton Rating Scale for depression scores following treatment. Responders had significantly larger P2 slopes prior to treatment than did nonresponders. P2 slopes did not differ significantly between responders and nonresponders following antidepressant treatment. These data support the conclusion that P2 amplitude/intensity slope may be a predictor of response to treatment with antidepressant medication.
Although neurocognitive impairment has been widely reported in major depressive disorder (MDD), confounding factors, such as the effects of psychotropic medication, have rarely been controlled for.
To examine neurocognitive function in medication-free patients with MDD and healthy controls.
Forty-four patients meeting DSM-IV criteria for MDD, all psychotropic-medication-free for at least 6 weeks, and 44 demographically matched, healthy comparison subjects completed a comprehensive neurocognitive battery.
Patients with depression were impaired significantly in a range of cognitive domains, including attention and executive function and visuospatial learning and memory, compared with controls. Motor and psychomotor functions were intact. Severity of depression correlated with learning and memory performance, but not executive function.
Pronounced neurocognitive impairment was found in this sample of young adult out-patients with MDD. This is not attributable to the confounding effects of psychotropic medication and could therefore provide an objective marker of brain dysfunction in depression.
The catechol-O-methyltransferase (COMT) is a major degrading enzyme in the metabolic pathways of catecholaminergic neurotransmitters such as dopamine and norepinephrine. This study investigated whether the functionally relevant Val(108/158)Met gene variant is associated with differential antidepressant response to mirtazapine and/or paroxetine in 102 patients with major depression (DSM-IV criteria) participating in a randomized clinical trial with both drugs. In patients treated with mirtazapine, but not paroxetine, allelic variations in the COMT gene were associated with differential response. COMT(VAL/VAL) and COMT(VAL/MET) genotype carriers showed a better response than COMT(MET/MET)-bearing patients in the mirtazapine group. Moreover, carriers of the COMT(VAL/VAL) or COMT(VAL/MET) genotype had significantly greater HAMD-17 (Hamilton Rating Scale for Depression 17 item version) score reductions than COMT(MET/MET) homozygotes from week 2 to 6, respectively, in the mirtazapine group. Time course of response and antidepressant efficacy of mirtazapine, but not paroxetine, seem to be influenced in a clinically relevant manner by this allelic variation within the COMT gene.
In several previous biochemical, pharmacological, and genetic studies, the catechol-O-methyltransferase (COMT) has been suggested to be involved in the pathogenesis as well as the pharmacological treatment of affective disorders. In the present study, 256 patients with major depression (DSM-IV) of Caucasian descent were genotyped for the functional COMT val158met polymorphism and characterized for clinical response to antidepressive pharmacological treatment as measured by intra-individual changes of Hamilton Depression (HAM-D-21) scores over 6 weeks. The COMT 158val/val genotype conferred a significant risk of worse response after 4-6 weeks of antidepressant treatment in patients with major depression (week 4: p=0.003; week 5: p<0.0001; week 6: p<0.0001) after Bonferroni correction for multiple comparisons. The present results strongly point toward a negative influence of the higher activity COMT 158val/val genotype on antidepressant treatment response during the first 6 weeks of pharmacological treatment in major depression, possibly conferred by consecutively decreased dopamine availability. This finding suggests a potentially beneficial effect of an antidepressive add-on therapy with substances increasing dopamine availability individually tailored according to COMT val158met genotype.
Objective: Databases bring together diverse information in neuroimaging and psychiatry. They usually aim for both size and diversity of measures. The present article outlines the potential insights from the first entirely standardized and centralized International Brain Database.
Method: The database consists of data from over 1000 normal subjects (age range 6-70 years) and a growing number of age-matched patients with a psychiatric illness, acquired from seven laboratories (New York, Rhode Island, London, Holland, Adelaide, Melbourne and Sydney). It is an ‘integrative’ neuroimaging (electroencephalography (EEG), event-related potentials (ERP), structural and functional magnetic resonance imaging (sMRI, fMRI)), psychometric, demographic and genomic database.
Results: The most notable relationships in normal controls thus far include (i) an association between grey matter volume and EEG alpha frequency in frontal regions; (ii) a systematic reduction with age in cortical arousal (EEG power), speed of processing (ERP components) and most aspects of cognitive function, particularly for >50 years; (iii) a greater cortical arousal in female versus male subjects, but slower speed of processing; and (iv) a dissociation between speed (greater in male subjects) and accuracy/verbal processing (greater in female subjects) for psychological tasks. There is potential to explore the specificity of findings in psychiatric disorders in this international standardized database.
Conclusions: The size of this database has allowed for statistical tests of greater power than normal. The combination of size and diversity of measure has broader significance in providing a normative framework for evidence-based psychiatric research. It enables control for widespread individual differences, enhancing investigations of the sensitivity and specificity of brain findings, and the efficacy of medication in psychiatric disorders.
Introduction. e Previous studies of patients with unipolar depression have shown that early decrease of prefrontal EEG cordance in theta band can predict clinical response to various antidepressants. We have now examined whether decrease of prefrontal quantitative EEG (QEEG) cordance value after 1 week of venlafaxine treatment predicts clinical response to venlafaxine in resistant patients. Method. e We analyzed 25 inpatients who finished 4-week venlafaxine treatment. EEG data were monitored at baseline and after 1 week of treatment. QEEG cordance was computed at three frontal electrodes in theta frequency band. Depressive symptoms and clinical status were assessed using MontgomeryeÅ sberg Depression Rating Scale (MADRS), Beck Depression Inventory-Short Form (BDI-S) and Clinical Global Impression (CGI). Results. e Eleven of 12 responders (reduction of MADRS !50%) and only 5 of 13 non-responders had decreased prefrontal QEEG cordance value after the first week of treatment (p ¼ 0.01). The decrease of prefrontal cordance after week 1 in responders was significant (p ¼ 0.03) and there was no significant change in non-responders. Positive and negative predictive values of cordance reduction for response were 0.7 and 0.9, respectively. Conclusion. e The reduction of prefrontal theta QEEG cordance value after first week of treatment might be helpful in the prediction of response to venlafaxine.
Introduction Assumptions EM and Inference by Data Augmentation Methods for Normal Data More on the Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data Further Topics Appendices References Index
To investigate the concurrent validity and reliability of the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID), a short structured diagnostic interview for DSM-IV and ICD-10 psychiatric disorders in children and adolescents.
Participants were 226 children and adolescents (190 outpatients and 36 controls) aged 6 to 17 years. To assess the concurrent validity of the MINI-KID, participants were administered the MINI-KID and the Schedule for Affective Disorders and Schizophrenia for School Aged Children-Present and Lifetime Version (K-SADS-PL) by blinded interviewers in a counterbalanced order on the same day. Participants also completed a self-rated measure of disability. In addition, interrater (n = 57) and test-retest (n = 83) reliability data (retest interval, 1-5 days) were collected, and agreement between the parent version of the MINI-KID and the standard MINI-KID (n = 140) was assessed. Data were collected between March 2004 and January 2008.
Substantial to excellent MINI-KID to K-SADS-PL concordance was found for syndromal diagnoses of any mood disorder, any anxiety disorder, any substance use disorder, any ADHD or behavioral disorder, and any eating disorder (area under curve [AUC] = 0.81-0.96, kappa = 0.56-0.87). Results were more variable for psychotic disorder (AUC = 0.94, kappa = 0.41). Sensitivity was substantial (0.61-1.00) for 15/20 individual DSM-IV disorders. Specificity was excellent (0.81-1.00) for 18 disorders and substantial (> 0.73) for the remaining 2. The MINI-KID identified a median of 3 disorders per subject compared to 2 on the K-SADS-PL and took two-thirds less time to administer (34 vs 103 minutes). Interrater and test-retest kappas were substantial to almost perfect (0.64-1.00) for all individual MINI-KID disorders except dysthymia. Concordance of the parent version (MINI-KID-P) with the standard MINI-KID was good.
The MINI-KID generates reliable and valid psychiatric diagnoses for children and adolescents and does so in a third of the time as the K-SADS-PL.
Catechol-O-methyltransferase (COMT) inactivates norepinephrine and dopamine via methyl conjugation, and a G-A transition in the COMT gene (rs4680) influences the enzyme activity. It is a current area of debate whether rs4680 can influence antidepressant response in major depressive disorder, and whether this influence extends to bipolar depression. Chronotherapeutic interventions, such as sleep deprivation and light therapy, are multi-target in nature and are effective in bipolar depression. Here we studied the effect of rs4680 on response to sleep deprivation combined with light therapy (36 h awake followed by a night of undisturbed sleep, with 10,000 lx light administered for 30 min during the night awake and upon awakening) in 87 bipolar depressed inpatients. Patients who were homozygotic for the Val/Val variant showed a significantly less efficient antidepressant effect after the night awake than those who were heterozygotic and homozygotic for the Met variant. This effect of rs4680 is similar to its observed influence on response to serotonergic and noradrenergic drug treatments in major depressive disorder. This is the first study reporting an influence of rs4680 on antidepressant response in bipolar depression. This finding supports the hypothesis of a major role for catecholamines in the mechanism of action of chronotherapeutics, and for rs4680 in modulating this effect.
The brain-derived neurotrophic factor (BDNF) has been suggested to play a pivotal role in the aetiology of affective disorders. In order to further clarify the impact of BDNF gene variation on major depression as well as antidepressant treatment response, association of three BDNF polymorphisms [rs7103411, Val66Met (rs6265) and rs7124442] with major depression and antidepressant treatment response was investigated in an overall sample of 268 German patients with major depression and 424 healthy controls. False discovery rate (FDR) was applied to control for multiple testing. Additionally, ten markers in BDNF were tested for association with citalopram outcome in the STAR*D sample. While BDNF was not associated with major depression as a categorical diagnosis, the BDNF rs7124442 TT genotype was significantly related to worse treatment outcome over 6 wk in major depression (p = 0.01) particularly in anxious depression (p = 0.003) in the German sample. However, BDNF rs7103411 and rs6265 similarly predicted worse treatment response over 6 wk in clinical subtypes of depression such as melancholic depression only (rs7103411: TT < CC, p = 0.003; rs6265: GG < AA, p = 0.001). All SNPs had main effects on antidepressant treatment response in ANOVA models when the remaining SNPs were considered as covariates. The STAR*D analyses did not yield significant results at any of the ten BDNF markers. Our results do not support an association between genetic variation in BDNF and antidepressant treatment response or remission. Post-hoc analyses provide some preliminary support for a potential minor role of genetic variation in BDNF and antidepressant treatment outcome in the context of melancholic depression.
Several reports have been published investigating the relationship between common variants in serotonin-related candidate genes and antidepressant response, and most of the results have been equivocal. We previously reported a significant association between variants in serotonin-related genes and response to the selective serotonin reuptake inhibitor fluoxetine. Here, we attempt to expand upon and replicate these results by (i) resequencing the exonic and putatively regulatory regions of five serotonin-related candidate genes (HTR1A, HTR2A, TPH1, TPH2, and MAOA) in our fluoxetine-treated sample to uncover novel variants; (ii) selecting tagging single nucleotide polymorphisms (SNPs) for these genes from the resequencing data; and (iii) evaluating these tagging SNPs for association with response to the selective serotonin reuptake inhibitor citalopram in an independent sample of participants who are enrolled in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) clinical study (N=1953). None of the variants associated previously with fluoxetine response were found to be associated with citalopram response in the STAR*D sample set. Nor were any of the additional tagging SNPs found to be associated with citalopram response. An additional SNP in HTR2A (rs7997012), previously reported to be associated with outcome of citalopram treatment in this sample, but not well tagged by any of the other SNPs we studied, was also genotyped, and was associated with citalopram response (P=0.0002), strongly supporting the previous observation in the same STAR*D sample. Our results suggest that resequencing the serotonin-related genes did not identify any additional common SNPs that have not been identified previously. It appears that genetic variation in these five genes has a marginal effect on response to citalopram, although a previously observed association was supported and awaits replication in an independent sample.
The Hamilton Depression Rating Scale (HDRS) is the most widely used scale for patient selection and follow-up in research studies of treatments of depression. Despite extensive study of the reliability and validity of the total scale score, the psychometric characteristics of the individual items have not been well studied. In the only reliability study to report agreement on individual items using a test-retest interview method, most of the items had only fair or poor agreement. Because this is due in part to variability in the way the information is obtained to make the various rating distinctions, the Structured Interview Guide for the HDRS (SIGH-D) was developed to standardize the manner of administration of the scale. A test-retest reliability study conducted on a series of psychiatric inpatients demonstrated that the use of the SIGH-D results in a substantially improved level of agreement for most of the HDRS items.
Patients diagnosed with major depressive disorder (MDD) and enrolled in an open-label safety surveillance study of a sustained release formulation of bupropion hydrochloride (100 to 300 mg/day) were evaluated with the Hamilton Rating Scale for Depression (HAM-D) immediately before and 6 to 12 weeks after the initiation of drug treatment. Auditory event-related potentials (ERPs) recorded under a stimulus intensity modulation paradigm were also obtained at these times. Patients were classified as responders and nonresponders based on post-treatment HAM-D scores, with responders having HAM-D scores less than 10 and nonresponders having scores greater than 10. Consistent with our previous findings, responders exhibited significantly larger positive slope coefficients for P2 ERP component amplitudes as a function of auditory stimulus intensity obtained at baseline and were not affected by bupropion treatment. Thus, these results further support our previous finding that ERP amplitude/intensity functions measured under a stimulus intensity modulation paradigm provide information about the likelihood of a positive therapeutic response to antidepressant pharmacotherapy in patients with MDD and extends these results to bupropion, a pharmacologically atypical antidepressant agent.
Brain event-related potentials (ERPs) to probe tones in a dichotic complex tone test were recorded from right-handed depressed patients (n = 44) and normal subjects (n = 19) at homologous sites over left and right hemispheres (F3, F4; C3, C4; P3, P4; O1, O2). There were no differences between groups N1 or P2 amplitude, but patients had smaller P3 amplitude than did normal subjects. Depressed patients failed to show either the left ear advantage or behavior-related hemispheric asymmetry of P3 seen for normal subjects. Depressed patients also showed less difference in hemispheric asymmetry between same and different judgments. These findings indicate that the abnormal behavioral asymmetry for dichotic pitch discrimination in depressed patients reflects a reduction in hemispheric asymmetry and is related to relatively late stages of cognitive processing.
The biological substrate of the antidepressant effect of total sleep deprivation (TSD) has not yet been elucidated. Furthermore, electrophysiological predictors for the response to TSD have not been studied extensively. The aim of present study was to examine the changes in acoustically evoked potentials (N1, P2, N2, P300) after a night of sleep deprivation and to analyze differences between responders and nonresponders. 17 depressive inpatients were studied. The most prominent changes in auditory evoked potentials (responders and nonresponders) were found for the amplitude of the P300 component. Differences between responders and nonresponders could be established for the amplitude and latencies in the N1 component. Responders showed smaller N1 amplitudes before TSD but a higher increase after TSD than nonresponders.
The purpose of this study was to examine the utility of quantitative electroencephalography (QEEG) in the prediction of response to imipramine in depressed patients. Forty patients with a diagnosis of unipolar depression were subjected to a placebo washout and were assessed at pre-drug, 3 h after their first dose of imipramine, and again 2 weeks into treatment. Following 4 weeks of open imipramine treatment, patients were separated into responder (R) and non-responder (NR) groups. Statistical analysis of the 29 patients who completed the study focused on group comparisons of power spectral estimates in four frequency bands from multi-channel recordings. Results showed that theta power differentiated R and NR groups prior to treatment, in response to an acute test dose, as well as after 2 weeks of active drug treatment. Results based on this exploratory study suggest that QEEG may be a useful early predictor of response to imipramine.
Increased slow-wave and decreased fast-wave activity on the electroencephalogram is common in brain dysfunction and may be caused by partial cortical deafferentation. No measure that is specific or sensitive for this deafferentation, however, has yet been reported. We studied a series of subjects with white-matter lesions undercutting the cortex and developed a method for analyzing electrical activity called "cordance" that has face validity as a measure of cortical deafferentation. Cordance is measured along a continuum of values: positive values denote "concordance," an indicator associated with normally functioning brain tissue; negative values denote "discordance," an indicator associated with undercutting lesions, low perfusion, and low metabolism. We present a series of subjects studied with magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography that demonstrate strong associations between cordance and other measures of brain structure and function.
As a modification of the diagnostic criteria of the serotonin syndrome proposed by Sternbach, we developed the Serotonin syndrome scale for the operationalized assessment of both the presence and the severity of the core symptoms of the serotonin syndrome. In a first study on the validity of this scale, the relationships between the serotonin syndrome score (SSS) and both the paroxetine plasma levels (n = 42) and the loudness dependence of the auditory evoked potentials (LDAEP; n = 24) were investigated in depressed patients treated with paroxetine. A strong LDAEP is supposed to indicate low central serotonergic neurotransmission, and vice versa. The SSS was positively related to paroxetine plasma levels and negatively to the LDAEP. Both results support the validity of the serotonin syndrome scale. Using a SSS > 6 as diagnostic criterion, mild serotonin syndromes were diagnosed in 5 of our 42 patients. The Serotonin syndrome scale may become a useful tool for clinicians and scientists dealing with the serotonin syndrome.
This study investigated the relationship of clinical, neuropsychological, and electrophysiological measures of prefrontal dysfunction with treatment response in elderly patients with major depression.
Forty-nine depressed elderly subjects were studied before and after 6 weeks of adequate antidepressant treatment and compared with 22 psychiatrically normal controls. The psychomotor retardation item of the Hamilton Depression Rating Scale, the initiation/perseveration subscore of the Mattis Dementia Rating Scale, and the latency of the P300 auditory evoked potential were used as indices of prefrontal dysfunction. The intensity of antidepressant drug treatment was classified and monitored for a 6-week period.
Abnormal initiation/perseveration score, psychomotor retardation, and long P300 latency predicted 58% of the variance in change of depression scores from baseline to 6 weeks (F3= 20.1, P<.001). Depressed patients who remained symptomatic (n = 25) had more abnormal initiation/perseveration scores and longer P300 latency compared with depressed patients who achieved remission (n = 24) and control subjects. There were no differences between the last 2 groups. The association between psychomotor retardation, initiation/perseveration scores, P300 latency, and response to antidepressant treatment could not be explained by differences in demographic and clinical characteristics or treatment intensity between remitted and nonremitted depressed patients.
Prefrontal dysfunction was associated with poor or delayed antidepressant response in depressed elderly patients. This observation, if confirmed, may aid clinicians in identifying candidates for aggressive somatic therapies and for interventions offering structure of daily activities.