Clinical Characteristics and Neuroanatomical Predictors of Acute Antidepressant Outcome for Patients with Comorbid Depression and Mild Cognitive Impairment

Background: Older adults presenting with both a depressive disorder (DEP) and cognitive impairment (CI) represent a unique, understudied population. The classification of cognitive impairment severity continues to be debated though it has recently been subtyped into late (LMCI) versus early (EMCI) stages. Previous studies have found associations between treatment outcome and both cortical thickness and white matter hyperintensities (WMH), though report inconsistent directionality and affected regions. In this study, we examined baseline clinical characteristics and neuroanatomical features as prognostic indicators for older adults with comorbid DEP and CI participating in an open antidepressant trial. EMCI is hypothesized to have greater cortical thickness and global cognition than LMCI. Antidepressant treatment remitters and responders are hypothesized to have greater cortical thickness and lower WMH burden than non-remitters and non-responders. Methods: Key inclusion criteria were diagnosis of major depression or dysthymic disorder with Hamilton Depression Rating Scale (HDRS) score \u3e14, and cognitive impairment defined by MMSE score ≥21 and impaired performance on the WMS-R Logical Memory II test. Patients were classified as EMCI or LMCI based on the 1.5 SD cutoff on tests of verbal memory, and compared on baseline clinical, neuropsychological, and anatomical characteristics. All patients underwent a baseline MRI scan and received open antidepressant treatment for 8 weeks. Cortical thickness was extracted using an automated brain segmentation and reconstruction program (FreeSurfer). Vertex-wise analyses were conducted using general linear models to evaluate the relationships between cortical thickness and clinical variables. Results: 79 DEP-CI patients were recruited, of whom 39 met criteria for EMCI and 40 for LMCI. The mean age was 68.9 and mean HDRS was 23.0. LMCI patients had significantly worse global cognition and smaller right hippocampal volume compared to EMCI patients. EMCI patients had thicker right medial orbitofrontal cortex than LMCI. MRI indices of cerebrovascular disease did not differ between MCI subtypes. Remitters had greater deep WMH burden, left medial orbitofrontal gyrus thickness, and right superior frontal gyrus thickness than non-remitters. Greater HDRS depressive severity was positively correlated with right pars triangularis thickness. Stronger ADAS-Cog global cognitive performance was positively correlated with thickness in diffuse cortical areas. Conclusions: Cognitive and neuronal loss markers differed between EMCI and LMCI among patients with DEP-CI, with LMCI being more likely to have the clinical and neuronal loss markers known to be associated with Alzheimer’s disease. Samples of DEP-CI exhibit unique patterns of cortical thickness and WMHs compared to their non-CI peers. Cortical thickness may serve as predictor of treatment remission and relates to both depressive severity and global cognition

On the neurobiology of apathy and depression in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a cerebrovascular pathology that affects the small vessels of the brain, resulting in heterogeneous brain tissue changes. These can lead to neuropsychiatric symptoms such as apathy, a loss of motivation, and depression, which is characterised by low mood and a loss of pleasure. Apathy and depression are both prevalent symptoms in SVD, but an understanding of the relationship between underlying disease processes and the expression of these neuropsychiatric symptoms remains poor. This thesis uses magnetic resonance imaging techniques to examine the neurobiological basis of apathy and depression in SVD. We show that apathy is related to focal grey matter damage and distributed white matter microstructural change. These microstructural changes underlie large-scale white matter network disruption, which is related to apathy, but not depression. We then show that depression, as a construct, can be dissociated into distinct symptoms which are associated with overlapping and distinct areas of cortical atrophy over time. This suggests that depression as a general syndrome may be characterised by atrophy in core structures, while different symptoms are associated with atrophy in more specialised areas. Consistent with these patterns of overarching tissue damage, we find that apathy, but not depression, predicts conversion to dementia in patients with SVD. Our findings suggest that different types of SVD-related pathology lead to apathy and depression. Diffuse white matter damage may lead to widespread network disruption, resulting in apathy and cognitive impairment. In contrast, depressive symptoms are associated with focal patterns of grey matter atrophy over time. This highlights the importance of differentiating neuropsychiatric symptoms, and paves the way for targeted treatment approaches.Cambridge International Scholarship (Cambridge Trust)

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

Predicting trajectories of symptom change during and following treatment in adolescents with Unipolar Major Depression.

Objective: Definitions of treatment response used in randomised controlled trials for unipolar major depression are non-standardised and arbitrary. Proportion of non-responders has been estimated as ranging from 20%-40% across such trials. I aimed to classify depressed adolescents recruited to the UK IMPACT trial into different trajectories of depression symptom response using a longitudinal data-driven approach: growth mixture modelling (GMM) and investigate potential predictors of trajectory classes in this cohort. Method: 465 depressed adolescents received manualised psychological therapies in the IMPACT trial. GMM was used to plot the trajectories of self-reported depressive symptoms measured at 6 nominal time points over 86 weeks from randomisation, and categorise patients into their most likely trajectory class. Chapters 2-4 investigated the prognostic value of a number of variables. Chapter 2 investigated a battery of demographic and clinical variables including subclinical psychotic symptoms. Chapter 3 focused on a subsample of patients: 109 of the 465 with structural magnetic resonance imaging (MRI) data. FreeSurfer was used to extract cortical thickness (CT) and surface area (SA) measures from 4 regions of interest (ROI; rostral anterior cingulate, dorsolateral prefrontal cortex, orbitofrontal cortex, and insular cortex). Chapter 4 focused on another subsample of patients: 166 of the 465 with salivary basal cortisol data at both waking and evening. Logistic regressions were used in Chapters 2-4 to investigate whether these variables were associated with increased likelihood of membership to a certain GMM class. Results: A piecewise GMM categorised patients into two classes with initially similar and subsequently distinct trajectories. Both groups had a significant decline in depressive symptoms over the first 18 weeks. Eighty-four per cent of patients were classed as “continued-improvers” through reporting an improvement in symptoms over the full duration of the study. A further class of 15.9% of patients were termed “halted-improvers” who had higher depression scores at baseline, faster recovery but no further improvement after 18 weeks. This data-driven method of classification showed only moderate agreement with a priori classification methods, and suggested misclassification rate could be as great as 31%. Co-morbid psychiatric disorders at baseline moderately increased the liability of being a member of the halted-improvers class (OR = 1.40, CI 1.00-1.96). No other clinical, neurological or cortisol variable reached statistical significance for predicting trajectory class. Conclusion: A fast reduction in depressive symptoms in the first few weeks of treatment may not indicate a good prognosis. Further, halted-improvement may not be apparent until after 18 weeks of treatment. Capitalizing on repeated symptom assessments with longitudinal data driven modelling may improve the precision of revealing patient groups with differential responses to treatment. Further work should seek to validate these trajectories in a separate sample of adolescents.The IMPACT study was funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme (project number 06/05/01). MR-IMPACT was funded by the Medical Research Council (grant: G0802226) and IMPACT-GH was funded by the Evelyn Trust. My doctoral studentship was awarded by the Neuroscience in Psychiatry (NSPN.Org) Consortium itself funded by a strategic award from the Wellcome Trust (095844/Z/11/Z) awarded to Professor Ian Goodyer and Professor Peter Fonagy

Investigating the neural substrates of gambling disorder using multiple neuromodulation and neuroimaging approaches

Introduction : Le trouble du jeu de hasard et d'argent (GD) est caractérisé par un comportement de jeu inadapté qui interfère avec les activités personnelles ou professionnelles. Ce trouble psychiatrique est difficile à traiter avec les thérapies actuelles et les rechutes sont fréquentes. Les symptômes dépressifs et cognitifs (e.g., l'impulsivité), ainsi que le "craving" (désir intense de jouer) sont des facteurs prédictifs de rechutes. Une meilleure compréhension des substrats neuronaux et leurs significations cliniques pourraient mener au développement de nouveaux traitements. La stimulation transcrânienne à courant direct (tDCS) pourrait être l'un de ceux-ci car elle permet de cibler des circuits neuronaux spécifiques. De plus, la tDCS ciblant le cortex dorsolatéral préfrontal (DLPFC) pourrait améliorer les symptômes dépressifs et cognitifs et réduire le craving. Cependant, les effets précis de la tDCS sur la fonction cérébrale, ainsi que leurs significations cliniques, demeurent à être élucidés. Par ailleurs, étant donné que les patients avec GD présentent souvent des différences morphométriques par rapport aux individus en santé, il est possible de faire l'hypothèse que la morphométrie cérébrale influence les effets de la tDCS. Objectifs : Ce travail avait trois objectifs principaux. Le premier objectif était d'explorer s'il y avait des associations entre les substrats neuronaux et les symptômes cliniques et cognitifs. Le deuxième objectif était d'examiner les effets de la tDCS sur la fonction cérébrale. Le troisième objectif était d'explorer si la morphométrie du site de stimulation (DLPFC) pouvait influencer les effets de la tDCS sur les substrats neuronaux. Méthode : Nous avons réalisé quatre études différentes. Dans la première étude, nous avons mesuré la morphométrie cérébrale en utilisant l'imagerie par résonance magnétique (IRM) structurelle. Nous avons mesuré les corrélations entre la morphométrie et les symptômes cliniques (dépression, sévérité et durée du GD) et cognitifs (impulsivité). De plus, nous avons comparé la morphométrie des patients à celui d'une base de données normative (individus en santé) en contrôlant pour plusieurs facteurs comme l'âge. Dans la deuxième étude, nous avons mesuré la fonction cérébrale (connectivité fonctionnelle) des patients avec l'IRM fonctionnelle. Nous avons examiné s'il y avait des liens entre la connectivité fonctionnelle et les symptômes cognitifs (impulsivité et prise de risque) et cliniques (sévérité et durée du GD). Dans la troisième étude, nous avons étudié les effets de la tDCS sur la connectivité fonctionnelle et si la morphométrie du DLPFC pouvait influencer ces effets. Dernièrement, dans la quatrième étude, nous avons examiné si la morphométrie du DLPFC pouvait influencer les effets de la tDCS sur la neurochimie (avec la spectroscopie par résonance magnétique). Résultats : Nous avons démontré deux corrélations positives entre la superficie du cortex occipital et les symptômes dépressifs (étude I). Nous avons également mis en évidence une corrélation positive entre la connectivité fonctionnelle d'un réseau occipital et l'impulsivité (étude II). De plus, il y avait une corrélation positive entre la connectivité fonctionnelle de ce réseau et la sévérité du GD. Par ailleurs, il y avait des corrélations positives entre la connectivité fonctionnelle de l'opercule frontal droit et la prise de risque (étude II). En outre, la connectivité fonctionnelle d'un réseau cérébelleux était corrélée avec les symptômes dépressifs (étude II). Les patients avaient aussi plusieurs différences morphométriques par rapport aux individus en santé (cortex occipital, préfrontal, etc.). Nous avons démontré également que la tDCS appliquée sur le DLPFC a augmenté la connectivité fonctionnelle d'un réseau fronto-pariétal (étude III). Finalement, cette thèse a montré que la morphométrie du DLPFC influence les augmentations induites par la tDCS sur la connectivité fonctionnelle du réseau fronto-pariétal (étude III) et le niveau de GABA frontal (étude IV). Conclusions : Cette thèse démontre une importance clinique potentielle pour les régions occipitales, frontales et cérébelleuses, particulièrement pour les patients ayant des symptômes dépressifs ou cognitifs. De plus, elle montre que la tDCS peut renforcer le fonctionnement d'un réseau fronto-pariétal connu pour son rôle dans les fonctions exécutives. Il reste à déterminer si un plus grand nombre de sessions pourrait apporter des bénéfices cliniques additionnels afin d'aider les patients à résister le jeu. Finalement, les résultats de cette thèse suggèrent que la morphométrie des régions sous les électrodes pourrait aider à identifier les meilleurs candidats pour la tDCS et pourrait être considéré pour la sélection des cibles de stimulation.Introduction: Gambling disorder (GD) is characterised by maladaptive gambling behaviour that interferes with personal or professional activities. This psychiatric disorder is difficult to treat with currently available treatments and relapse rates are high. Several factors can predict relapse, including depressive and cognitive (e.g., impulsivity, risk taking) symptoms, in addition to craving (strong desire to gamble). A better understanding of neural substrates and their clinical significance could help develop new treatments. Transcranial direct current stimulation (tDCS) might be one of these since it can target specific neural circuits. In addition, tDCS targeting the dorsolateral prefrontal cortex (DLPFC) could improve depressive and cognitive symptoms as well as reduce craving. However, the precise effects of tDCS on brain function, as well as their clinical significance, remain to be elucidated. Furthermore, considering that patients with GD often display morphometric differences as compared to healthy individuals, it may be worth investigating whether brain morphometry influences the effects of tDCS. Objectives: This work had three main objectives. The first objective was to explore whether there were associations between neural substrates and clinical and cognitive symptoms. The second objective was to examine the effects of tDCS on brain function. The third objective was to explore whether morphometry of the stimulation site (DLPFC) influenced the effects of tDCS on neural substrates. Methods: We carried out four different studies. In the first study, we investigated brain morphometry using structural magnetic resonance imaging (MRI). We tested for correlations between morphometry and clinical symptoms (depression, GD severity, GD duration) and cognitive symptoms (impulsivity). In addition, we compared the morphometry of patients with GD to that of a normative database (healthy individuals) while controlling for several factors such as age. In a second study, we assessed brain function (functional connectivity) in patients with functional MRI (fMRI). We examined whether there were associations between brain function and cognitive symptoms (impulsivity and risk taking) as well as clinical symptoms (GD severity and duration). In the third study, we examined tDCS-induced effects on brain function and whether morphometry of the DLPFC influenced these effects. Lastly, in the fourth study, we examined whether DLPFC morphometry influenced tDCS-induced effects on neurochemistry (using magnetic resonance spectroscopy imaging). Results: Firstly, we found two positive correlations between surface area of the occipital cortex and depressive symptoms (study I). We also showed a positive correlation between functional connectivity of an occipital network and impulsivity (study II). In addition, there was a positive correlation between functional connectivity of this network and GD severity (study II). In addition, there were positive correlations between functional connectivity of the right frontal operculum and risk-taking (study II). Also, functional connectivity of a cerebellar network was positively correlated with depressive symptoms (study II). Moreover, patients with GD had several morphometric differences as compared to healthy individuals (occipital and prefrontal cortices, etc.). Furthermore, we observed that tDCS over the DLPFC increased functional connectivity of a fronto-parietal circuit during stimulation (study III). Lastly, this thesis indicated that DLPFC morphometry influenced tDCS-induced elevations on fronto-parietal functional connectivity (study III) and frontal GABA levels (study IV). Conclusions: This thesis suggests the potential clinical relevance of occipital, frontal, and cerebellar regions, particularly for those with depressive and cognitive symptoms. It also indicates that tDCS can strengthen the functioning of a fronto-parietal network known to be implicated in executive functions. It remains to be seen whether a greater number of tDCS sessions could lead to clinical benefits to help patients resist gambling. Finally, the results of this thesis suggest that morphometry of the regions under the electrodes might help predict better candidates for tDCS and could be considered to select stimulation targets

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig