The resurgence of interest in anhedonia within major depression has been fuelled by clinical trials demonstrating its utility in predicting antidepressant response as well as recent ...conceptualizations focused on the role and manifestation of anhedonia in depression. Historically, anhedonia has been understood as a "loss of pleasure", yet neuropsychological and neurobiological studies reveal a multifaceted reconceptualization that emphasizes different facets of hedonic function, including desire, effort/motivation, anticipation and consummatory pleasure. To ensure generalizability across studies, evaluation of the available subjective and objective methods to assess anhedonia is necessary. The majority of research regarding anhedonia and its neurobiological underpinnings comes from preclinical research, which uses primary reward (e.g. food) to probe hedonic responding. In contrast, behavioural studies in humans primarily use secondary reward (e.g. money) to measure many aspects of reward responding, including delay discounting, response bias, prediction error, probabilistic reversal learning, effort, anticipation and consummatory pleasure. The development of subjective scales to measure anhedonia has also increased in the last decade. This review will assess the current methodology to measure anhedonia, with a focus on scales and behavioural tasks in humans. Limitations of current work and recommendations for future studies are discussed.
While there is clearly an association between improvement in depressive symptoms and functioning, symptom improvement can also be dissociated from functional improvement and work loss.3,4 Hence there ...has been increasing recognition that symptomatic remission is an insufficient goal of treatment for MDD and that return to premorbid psychosocial functioning should be targeted.5 Cognitive dysfunction refers to deficits in attention, verbal and nonverbal learning, short-term and working memory, visual and auditory processing, problem solving, processing speed, and motor functioning. Numerous pharmacotherapy augmentation strategies have shown efficacy in MDD, including lithium52 and atypical APs,53 but cognitive dysfunction has not been specifically examined in these studies. ...there is evidence that lithium is associated with adverse cognitive side effects that may negatively impact psychosocial functioning.54 Likewise, there is evidence to suggest that, in BD, atypical APs may worsen cognitive performance.55,56 Psychostimulants may be expected to have positive effects on cognition, but augmentation studies of osmotic-release oral system-methylphenidate57,58 and lisdexamfetamine59,60 in MDD have shown inconsistent results on depressive symptoms and subjective neurocognitive measures, although the latter were not always measured.
Despite its high toll on society, there has been little recent improvement in treatment efficacy for major depressive disorder (MDD). The identification of biological markers of successful treatment ...response may allow for more personalized and effective treatment. Here we investigate whether resting-state functional connectivity predicted response to treatment with repetitive transcranial magnetic stimulation (rTMS) to dorsomedial prefrontal cortex (dmPFC). Twenty-five individuals with treatment-refractory MDD underwent a 4-week course of dmPFC-rTMS. Before and after treatment, subjects received resting-state functional MRI scans and assessments of depressive symptoms using the Hamilton Depresssion Rating Scale (HAMD17). We found that higher baseline cortico-cortical connectivity (dmPFC-subgenual cingulate and subgenual cingulate to dorsolateral PFC) and lower cortico-thalamic, cortico-striatal, and cortico-limbic connectivity were associated with better treatment outcomes. We also investigated how changes in connectivity over the course of treatment related to improvements in HAMD17 scores. We found that successful treatment was associated with increased dmPFC-thalamic connectivity and decreased subgenual cingulate cortex-caudate connectivity, Our findings provide insight into which individuals might respond to rTMS treatment and the mechanisms through which these treatments work.
Current practice for selecting pharmacological and non-pharmacological antidepressant treatments has yielded low response and remission rates in Major Depressive Disorder (MDD). Neuroimaging ...biomarkers of brain structure and function may be useful in guiding treatment selection by predicting response vs. non-response outcomes.
In this review, we summarize data from studies examining predictors of treatment response using structural and functional neuroimaging modalities, as they pertain to pharmacotherapy, psychotherapy, and stimulation treatment strategies. A literature search was conducted in OVID Medline, EMBASE, and PsycINFO databases with coverage from January 1990 to January 2017.
Several imaging biomarkers of therapeutic response in MDD emerged: frontolimbic regions, including the prefrontal cortex, anterior cingulate cortex, hippocampus, amygdala, and insula were regions of interest. Since these sub-regions are implicated in the etiology of MDD, their association with response outcomes may be the result of treatments having a normalizing effect on structural or activation abnormalities.
The direction of findings is inconsistent in studies examining these biomarkers, and variation across ‘biotypes’ within MDD may account for this. Limitations in sample size and differences in methodology likely also contribute.
The identification of accurate, reliable neuroimaging biomarkers of treatment response holds promise toward improving treatment outcomes and reducing burden of illness for patients with MDD. However, before these biomarkers can be translated into clinical practice, they will need to be replicated and validated in large, independent samples, and integrated with data from other biological systems.
•Neuroimaging response predictors for antidepressant therapies are proposed.•Frontolimbic structural and functional indices may have predictive potential.•Frontolimbic regions are also involved in depression etiology.•Inconsistency of findings may result from variation across depression ‘biotypes’.•Data replication, validation, and integration are required for clinical translation.
Objective:
Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can ...limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management.
Methods:
This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance.
Results:
At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports.
Conclusion:
Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.
When the brain is not engaged in goal-directed activities and at rest, there are still measureable patterns of activity. One resting-state network, the default mode network (DMN) is responsible for a ...self-referential introspective state. There are many factors that influence normal changes in brain activity. The purpose of this review is to summarize differences in DMN functional connectivity in healthy individuals by age, sex, cognitive function, and analysis type to characterize what is "normal." Studies were systematically selected up to August 2016. Two reviewers independently used predetermined inclusion and exclusion criteria to identify relevant studies. Studies that provided sufficient information were included in a subsequent voxel-wise meta-analysis. Strength of DMN functional connectivity follows an inverse U-shape, where it is strongest in adulthood and lowest in children and elderly. Cognitive function is positively correlated with DMN functional connectivity. Females exhibit stronger intranetwork connectivity compared with males. Effects of analysis type were inconclusive and more studies need to incorporate complementing techniques. The voxel-wise meta-analysis was only conducted for the age factor. Findings supported an immature network in children compared with adults and a stronger network in adults compared with elderly. This is the first study to review differences of DMN functional connectivity in healthy individuals by age, sex, cognitive function, and analysis type. Findings add to the understanding of normal variance. Furthermore, defining a normal comparative base may allow for the identification of DMN change into pathology. This is important since it may allow for the detection of an intermediate risk phenotype and could serve as a biomarker for treatment response.
Treatment-resistant major depressive disorder is common; repetitive transcranial magnetic stimulation (rTMS) by use of high-frequency (10 Hz) left-side dorsolateral prefrontal cortex stimulation is ...an evidence-based treatment for this disorder. Intermittent theta burst stimulation (iTBS) is a newer form of rTMS that can be delivered in 3 min, versus 37·5 min for a standard 10 Hz treatment session. We aimed to establish the clinical effectiveness, safety, and tolerability of iTBS compared with standard 10 Hz rTMS in adults with treatment-resistant depression.
In this randomised, multicentre, non-inferiority clinical trial, we recruited patients who were referred to specialty neurostimulation centres based at three Canadian university hospitals (Centre for Addiction and Mental Health and Toronto Western Hospital, Toronto, ON, and University of British Columbia Hospital, Vancouver, BC). Participants were aged 18–65 years, were diagnosed with a current treatment-resistant major depressive episode or could not tolerate at least two antidepressants in the current episode, were receiving stable antidepressant medication doses for at least 4 weeks before baseline, and had an HRSD-17 score of at least 18. Participants were randomly allocated (1:1) to treatment groups (10 Hz rTMS or iTBS) by use of a random permuted block method, with stratification by site and number of adequate trials in which the antidepressants were unsuccessful. Treatment was delivered open-label but investigators and outcome assessors were masked to treatment groups. Participants were treated with 10 Hz rTMS or iTBS to the left dorsolateral prefrontal cortex, administered on 5 days a week for 4–6 weeks. The primary outcome measure was change in 17-item Hamilton Rating Scale for Depression (HRSD-17) score, with a non-inferiority margin of 2·25 points. For the primary outcome measure, we did a per-protocol analysis of all participants who were randomly allocated to groups and who attained the primary completion point of 4 weeks. This trial is registered with ClinicalTrials.gov, number NCT01887782.
Between Sept 3, 2013, and Oct 3, 2016, we randomly allocated 205 participants to receive 10 Hz rTMS and 209 participants to receive iTBS. 192 (94%) participants in the 10 Hz rTMS group and 193 (92%) in the iTBS group were assessed for the primary outcome after 4–6 weeks of treatment. HRSD-17 scores improved from 23·5 (SD 4·4) to 13·4 (7·8) in the 10 Hz rTMS group and from 23·6 (4·3) to 13·4 (7·9) in the iTBS group (adjusted difference 0·103, lower 95% CI −1·16; p=0·0011), which indicated non-inferiority of iTBS. Self-rated intensity of pain associated with treatment was greater in the iTBS group than in the 10 Hz rTMS group (mean score on verbal analogue scale 3·8 SD 2·0 vs 3·4 2·0 out of 10; p=0·011). Dropout rates did not differ between groups (10 Hz rTMS: 13 6% of 205 participants; iTBS: 16 8% of 209 participants); p=0·6004). The most common treatment-related adverse event was headache in both groups (10 Hz rTMS: 131 64% of 204; iTBS: 136 65% of 208).
In patients with treatment-resistant depression, iTBS was non-inferior to 10 Hz rTMS for the treatment of depression. Both treatments had low numbers of dropouts and similar side-effects, safety, and tolerability profiles. By use of iTBS, the number of patients treated per day with current rTMS devices can be increased several times without compromising clinical effectiveness.
Canadian Institutes of Health Research.
To define treatment response in depression as at least a 50% reduction in total symptom severity is to accept that up to half of patients will continue to have residual symptoms, most commonly low ...mood/loss of interest, cognitive problems, lack of energy, and difficulty sleeping. In fact, patients’ goals for treatment are to return to premorbid levels of functioning. This highlights the importance of assessing both functional outcomes and symptom improvement when evaluating the efficacy of antidepressant medication. Not all patients who achieve symptomatic response/remission will achieve a functional response/remission. In two studies (one with agomelatine and one with escitalopram), 54% of patients receiving agomelatine and 47% of those receiving escitalopram achieved a symptomatic response, and 53% of patients in each study achieved a functional response. However, 42% of patients receiving agomelatine and 35% of those receiving escitalopram had both a symptomatic and a functional response. The four symptoms of depression with the most marked effect on function are sad mood, impaired concentration, fatigue, and loss of interest. Low energy is particularly associated with poor occupational functioning, highlighting the importance of ongoing assessment of patients with depression, focusing particular attention on the symptoms that affect their ability to function, such as fatigue. Depending on the type of residual symptoms, some patients may benefit from combination therapy, such as adding dopamine modulator therapy. Antidepressant therapy is only effective if patients continue to take their medication, and high rates of early discontinuation have been reported. Therefore, when selecting treatment for depression, physicians can maximize the likelihood of adherence and persistence by taking into account both the antidepressant efficacy of treatment, its adverse effects and acceptability to patients.
Background Depression is a heterogeneous mental illness. Neurostimulation treatments, by targeting specific nodes within the brain’s emotion-regulation network, may be useful both as therapies and as ...probes for identifying clinically relevant depression subtypes. Methods Here, we applied 20 sessions of magnetic resonance imaging-guided repetitive transcranial magnetic stimulation (rTMS) to the dorsomedial prefrontal cortex in 47 unipolar or bipolar patients with a medication-resistant major depressive episode. Results Treatment response was strongly bimodal, with individual patients showing either minimal or marked improvement. Compared with responders, nonresponders showed markedly higher baseline anhedonia symptomatology (including pessimism, loss of pleasure, and loss of interest in previously enjoyed activities) on item-by-item examination of Beck Depression Inventory-II and Quick Inventory of Depressive Symptomatology ratings. Congruently, on baseline functional magnetic resonance imaging, nonresponders showed significantly lower connectivity through a classical reward pathway comprising ventral tegmental area, striatum, and a region in ventromedial prefrontal cortex. Responders and nonresponders also showed opposite patterns of hemispheric lateralization in the connectivity of dorsomedial and dorsolateral regions to this same ventromedial region. Conclusions The results suggest distinct depression subtypes, one with preserved hedonic function and responsive to dorsomedial rTMS and another with disrupted hedonic function, abnormally lateralized connectivity through ventromedial prefrontal cortex, and unresponsive to dorsomedial rTMS. Future research directly comparing the effects of rTMS at different targets, guided by neuroimaging and clinical presentation, may clarify whether hedonia/reward circuit integrity is a reliable marker for optimizing rTMS target selection.
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