Biology subjects

Hilland, E.

Publications and source records attributed to Hilland, E..

7 recordsLinked to original sources

bioRxiv2025

Thalamic Nuclei Volumes Across Psychiatric and Neurological Disorders: A Multi-Site Magnetic Resonance Imaging Study

The human thalamus is an integrative hub for multiple cortical and subcortical circuits involved in sensory processing and higher cognitive functions. Thalamic volume differences have been reported across multiple psychiatric and neurological disorders, but previous studies have typically relied on small samples, focused on one or a limited number of disorders, or investigated the thalamus as a whole without considering its functional subdivisions. In this multi-site study, we compared thalamic nuclei volumes across mild cognitive impairment (MCI), dementia (DEM), major depressive disorder, schizophrenia spectrum disorder (SCZ), clinical high risk for schizophrenia, bipolar spectrum disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder, Parkinsons disease, multiple sclerosis (MS), and healthy controls (N > 8 000). Using structural MRI, we segmented 25 bilateral thalamic nuclei, corresponding to six anatomical groups. Linear models revealed that anterior, medial and lateral regions of the thalamus were significantly smaller in several conditions, with largest effects observed for MCI, DEM, SCZ and MS. In contrast, the ventral and intralaminar groups were relatively normal. This pattern of effects largely corresponds to the canonical functional subdivision of the thalamus into higher-order and sensory regions. At the level of individual nuclei, the clinical conditions were associated with distinct patterns of alterations, while left and right lateral geniculate nuclei were implicated in six of the disorders, suggesting a possible relation with circadian and sleep disturbances. Together, the results highlight a role for the higher-order thalamus in common brain disorders and a differential involvement at the nuclei level, refining our understanding of thalamic pathology across common brain disorders.

neuroscience↗

bioRxiv2019

Exploring the Links between Specific Depression Symptoms and Brain Structure: A Network Study

The network approach to psychopathology has recently received considerable attention, and is as a novel way of conceptualizing mental disorders as causally interacting symptoms. In this study, we modeled a joint network of depression symptoms and depression-related brain structures, using 21 symptoms and five regional brain measures. We used a mixed sample of 268 individuals previously treated for one or more major depressive episodes and never depressed individuals. The network revealed associations between brain structure and unique depressive symptoms, which may clarify relationships regarding symptomatic and biological heterogeneity in depression.

neuroscience↗

bioRxiv2019

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

BackgroundPrevious structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression.\n\nMethodsWe used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state fMRI default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components.\n\nResultsUnivariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy.\n\nConclusionMultimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

neuroscience↗

bioRxiv2019

Predicting cognitive and mental health traits and their polygenic architecture using large-scale brain connectomics

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.\n\nSignificanceAlthough cognitive abilities and susceptibility to mental disorders reflect individual differences in brain function, neuroimaging is yet to provide a coherent account of the neuronal underpinnings. Here, we aimed to map the brain functional connectome of (i) cognitive and mental health traits and (ii) their polygenic architecture in a large population-based sample. We discovered high prediction accuracy for age and sex, and above-chance accuracy for educational attainment and intelligence (cognitive). In contrast, accuracies for dimensional measures of depression, anxiety and neuroticism (mental health), and polygenic scores across traits, were at chance level. These findings support the link between cognitive abilities and brain connectomics and provide a reference for studies mapping the brain connectomics of mental disorders and their genetic architectures.

neuroscience↗

bioRxiv2018

Attentional Bias Modification Alters fMRI Response towards Negative Stimuli in Residual Depression

BackgroundModification of attentional biases (ABM) may lead to more adaptive emotion perception and emotion regulation. Understanding the neural basis of these effects may lead to greater precision for future treatment development. Task-related fMRI following ABM training has so far not been investigated in depression. The main aim of the RCT was to explore differences in brain activity after ABM training in response to emotional stimuli.\n\nMethodsA total of 134 previously depressed individuals were randomized into 14 days of ABM- or a placebo training followed by an fMRI emotion regulation task. Depression symptoms and subjective ratings of perceived negativity during fMRI was examined between the training groups. Brain activation was explored within predefined areas (SVC) and across the whole brain. Activation in areas associated with changes in attentional biases (AB) and degree of depression was explored.\n\nResultsThe ABM group showed reduced activation within the amygdala and within the anterior cingulate cortex (ACC) when passively viewing negative images compared to the placebo group. No group differences were found within predefined SVCs associated with emotion regulation strategies. Response within the temporal cortices was associated with degree of change in AB and with degree of depressive symptoms in ABM versus placebo.\n\nLimitationsThe findings should be replicated in other samples of depressed patients and in studies using designs that allow analyses of within-group variability from baseline to follow-up.\n\nConclusionsABM training has an effect on brain function within circuitry associated with emotional appraisal and the generation of affective states.\n\nClinicaltrials.gov identifier: NCT02931487

clinical trials↗

bioRxiv2018

Effects of Attentional Bias Modification on Residual Symptoms in depression. A Randomized Controlled Trial.

BackgroundFollowing treatment, many depressed patients have significant residual symptoms. However, large randomised controlled trials (RCT) in this population are lacking. When Attention bias modification training (ABM) leads to more positive emotional biases, associated changes in clinical symptoms have been reported. A broader and more transparent picture of the true advantage of ABM based on larger and more stringent clinical trials have been requested.\n\nAimsTo evaluate the early effect of two weeks ABM training on blinded clinician-rated and self-reported residual symptoms, and whether changes towards more positive attentional biases (AB) would be associated with symptom reduction.\n\nMethodA total of 321 patients with a history of depression were included in a preregistered randomized controlled double-blinded trial. Patients were randomised to an emotional ABM paradigm over fourteen days or a closely matched control condition. Symptoms based on the Hamilton Rating Scale for Depression (HRSD) and Beck Depression Inventory II (BDI-II) were obtained at baseline and after ABM training.\n\nResultsABM training led to significantly greater decrease in clinician-rated symptoms of depression as compared to the control condition. No differences between ABM and placebo were found for self-reported symptoms. ABM induced a change of AB towards relatively more positive stimuli associated with greater symptom reduction.\n\nConclusionThe current study demonstrates that ABM produces early changes in both AB and blinded clinician-rated depressive symptoms. ABM may have practical potential in the treatment of residual depression.\n\nClinicalTrials.gov ID: NCT02658682

clinical trials↗

bioRxiv2018

Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

BackgroundDepression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data.\n\nMethodsWe assessed symptoms of depression and anxiety using Becks Depression and Becks Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imaging-based dynamic and static functional connectivity between subgroups in a subset of the total sample.\n\nResultsWe identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a fronto-temporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures.\n\nConclusionAdding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

neuroscience↗