| 1. |
- Dima, Danai, et al.
(författare)
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Subcortical volumes across the lifespan : Data from 18,605 healthy individuals aged 3-90 years.
- 2022
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 43:1, s. 452-469
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Tidskriftsartikel (refereegranskat)abstract
- Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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| 2. |
- Frangou, Sophia, et al.
(författare)
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Cortical thickness across the lifespan : Data from 17,075 healthy individuals aged 3-90 years
- 2022
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Ingår i: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 431-451
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Tidskriftsartikel (refereegranskat)abstract
- Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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| 3. |
- Wierenga, Lara M., et al.
(författare)
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Greater male than female variability in regional brain structure across the lifespan
- 2022
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Ingår i: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 470-499
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Tidskriftsartikel (refereegranskat)abstract
- For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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| 4. |
- Belov, Vladimir, et al.
(författare)
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
- 2024
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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| 5. |
- Hamilton, Paul J., et al.
(författare)
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Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder
- 2016
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Ingår i: Psychiatry Research. - : Elsevier. - 0925-4927 .- 1872-7506. ; 249, s. 91-96
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Tidskriftsartikel (refereegranskat)abstract
- Neural models of major depressive disorder (MDD) posit that over-response of components of the brains salience network (SN) to negative stimuli plays a crucial role in the pathophysiology of MDD. In the present proof-of-concept study, we tested this formulation directly by examining the affective consequences of training depressed persons to down-regulate response of SN nodes to negative material. Ten participants in the real neurofeedback group saw, and attempted to learn to down-regulate, activity from an empirically identified node of the SN. Ten other participants engaged in an equivalent procedure with the exception that they saw SN-node neurofeedback indices from participants in the real neurofeedback group. Before and after scanning, all participants completed tasks assessing emotional responses to negative scenes and to negative and positive self-descriptive adjectives. Compared to participants in the sham-neurofeedback group, from pre- to post-training, participants in the realneurofeedback group showed a greater decrease in SN-node response to negative stimuli, a greater decrease in self-reported emotional response to negative scenes, and a greater decrease in self-reported emotional response to negative self-descriptive adjectives. Our findings provide support for a neural formulation in which the SN plays a primary role in contributing to negative cognitive biases in MDD. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
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| 6. |
- Hamilton, Paul, et al.
(författare)
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Striatal dopamine deficits predict reductions in striatal functional connectivity in major depression: a concurrent C-11-raclopride positron emission tomography and functional magnetic resonance imaging investigation
- 2018
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Ingår i: Translational Psychiatry. - : NATURE PUBLISHING GROUP. - 2158-3188. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder (MDD) is characterized by the altered integration of reward histories and reduced responding of the striatum. We have posited that this reduced striatal activation in MDD is due to tonically decreased stimulation of striatal dopamine synapses which results in decremented propagation of information along the corticostriatal-pallido-thalamic (CSPT) spiral. In the present investigation, we tested predictions of this formulation by conducting concurrent functional magnetic resonance imaging (fMRI) and C-11-raclopride positron emission tomography (PET) in depressed and control (CTL) participants. We scanned 16 depressed and 14 CTL participants with simultaneous fMRI and C-11-raclopride PET. We estimated raclopride binding potential (BPND), voxel-wise, and compared MDD and CTL samples with respect to BPND in the striatum. Using striatal regions that showed significant between-group BPND differences as seeds, we conducted whole-brain functional connectivity analysis using the fMRI data and identified brain regions in each group in which connectivity with striatal seed regions scaled linearly with BPND from these regions. We observed increased BPND in the ventral striatum, bilaterally, and in the right dorsal striatum in the depressed participants. Further, we found that as BPND increased in both the left ventral striatum and right dorsal striatum in MDD, connectivity with the cortical targets of these regions (default-mode network and salience network, respectively) decreased. Deficits in stimulation of striatal dopamine receptors in MDD could account in part for the failure of transfer of information up the CSPT circuit in the pathophysiology of this disorder.
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| 7. |
- Petrov, Dmitry, et al.
(författare)
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Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
- 2017
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Ingår i: Machine learning in medical imaging. MLMI (Workshop). - Cham : Springer International Publishing. ; 10541, s. 371-378
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Tidskriftsartikel (refereegranskat)abstract
- As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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| 8. |
- Frost Bellgowan, Julie, et al.
(författare)
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A Neural Substrate for Behavioral Inhibition in the Risk for Major Depressive Disorder
- 2015
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Ingår i: Journal of the American Academy of Child and Adolescent Psychiatry. - : ELSEVIER SCIENCE BV. - 0890-8567 .- 1527-5418. ; 54:10, s. 841-848
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Behavioral inhibition (BI) is an early developing trait associated with cautiousness and development of clinical depression and anxiety. Little is known about the neural basis of BI and its predictive importance concerning risk for internalizing disorders. We looked at functional connectivity of the default-mode network (DMN) and salience network (SN), given their respective roles in self-relational and threat processing, in the risk for internalizing disorders, with an emphasis on determining the functional significance of these networks for BI. Method: We used functional magnetic resonance imaging to scan, during the resting state, children and adolescents 8 to 17 years of age who were either at high familial risk (HR; n = 16) or low familial risk (LR; n = 18) for developing clinical depression and/or anxiety. Whole-brain DMN and SN functional connectivity were estimated for each participant and compared across groups. We also compared the LR and HR groups on levels of BI and anxiety, and incorporated these data into follow-up neurobehavioral correlation analyses. Results: The HR group, relative to the LR group, showed significantly decreased DMN connectivity with the ventral striatum and bilateral sensorimotor cortices. Within the HR group, trait BI increased as DMN connectivity with the ventral striatum and sensorimotor cortex decreased. The HR and LR groups did not differ with respect to SN connectivity. Conclusion: Our findings show, in the risk for internalizing disorders, a negative functional relation between brain regions supporting self-relational processes and reward prediction. These findings represent a potential neural substrate for behavioral inhibition in the risk for clinical depression and anxiety.
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| 9. |
- Javaheripour, Nooshin, et al.
(författare)
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Altered resting-state functional connectome in major depressive disorder : a mega-analysis from the PsyMRI consortium
- 2021
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Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.
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| 10. |
- Sacchet, Matthew D., et al.
(författare)
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Cognitive and neural consequences of memory suppression in major depressive disorder
- 2017
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Ingår i: Cognitive, Affective, & Behavioral Neuroscience. - : SPRINGER. - 1530-7026 .- 1531-135X. ; 17:1, s. 77-93
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Tidskriftsartikel (refereegranskat)abstract
- Negative biases in cognition have been documented consistently in major depressive disorder (MDD), including difficulties in the ability to control the processing of negative material. Although negative information-processing biases have been studied using both behavioral and neuroimaging paradigms, relatively little research has been conducted examining the difficulties of depressed persons with inhibiting the retrieval of negative information from long-term memory. In this study, we used the think/no-think paradigm and functional magnetic resonance imaging to assess the cognitive and neural consequences of memory suppression in individuals diagnosed with depression and in healthy controls. The participants showed typical behavioral forgetting effects, but contrary to our hypotheses, there were no differences between the depressed and nondepressed participants or between neutral and negative memories. Relative to controls, depressed individuals exhibited greater activity in right middle frontal gyrus during memory suppression, regardless of the valence of the suppressed stimuli, and differential activity in the amygdala and hippocampus during memory suppression involving negatively valenced stimuli. These findings indicate that depressed individuals are characterized by neural anomalies during the suppression of long-term memories, increasing our understanding of the brain bases of negative cognitive biases in MDD.
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