Role of dopamine and gray matter density in aging effects and individual differences of functional connectomes.
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ABSTRACT:
SUBMITTER: Garzon B
PROVIDER: S-EPMC7981334 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Aging-related differences in white matter integrity, the presence of amyloid plaques, and density of biomarkers indicative of dopamine functions can be detected and quantified with in vivo human imaging. The primary aim of the present study was to investigate whether these imaging-based measures constitute independent imaging biomarkers in older adults, which would speak to the hypothesis that the aging brain is characterized by multiple independent neurobiological cascades. We assessed MRI-based markers of white matter integrity and PET-based marker of dopamine transporter density and amyloid deposition in the same set of 53 clinically normal individuals (age 65-87). A multiple regression analysis demonstrated that dopamine transporter availability is predicted by white matter integrity, which was detectable even after controlling for chronological age. Further post-hoc exploration revealed that dopamine transporter availability was further associated with systolic blood pressure, mirroring the established association between cardiovascular health and white matter integrity. Dopamine transporter availability was not associated with the presence of amyloid burden. Neurobiological correlates of dopamine transporter measures in aging are therefore likely unrelated to Alzheimer's disease but are aligned with white matter integrity and cardiovascular risk. More generally, these results suggest that two common imaging markers of the aging brain that are typically investigated separately do not reflect independent neurobiological processes. Hum Brain Mapp 37:621-631, 2016. © 2015 Wiley Periodicals, Inc.
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Project description:BackgroundDespite decades of research, there is continued uncertainty regarding whether autism is associated with a specific profile of gray matter (GM) structure. This inconsistency may stem from the widespread use of voxel-based morphometry (VBM) methods that combine indices of GM density and GM volume. If GM density or volume, but not both, prove different in autism, the traditional VBM approach of combining the two indices may obscure the difference. The present study measures GM density and volume separately to examine whether autism is associated with alterations in GM volume, density, or both.MethodsDifferences in MRI-based GM density and volume were examined in 6-25 year-olds with a diagnosis of autism spectrum disorder (n = 213, 80.8% male, IQ 47-154) and a typically developing (TD) sample (n = 190, 71.6% male, IQ 67-155). High-resolution T1-weighted anatomical images were collected on a single MRI scanner. Regional density and volume were estimated via whole-brain parcellation comprised of 1625 parcels. Parcel-wise analyses were conducted using generalized additive models while controlling the false discovery rate (FDR, q < 0.05). Volume differences in the 68-region Desikan-Killiany atlas derived from Freesurfer were also examined, to establish the generalizability of findings across methods.ResultsNo density differences were observed between the autistic and TD groups, either in individual parcels or whole brain mean density. Increased volume was observed in autism compared to the TD group when controlling for Age, Sex, and IQ, both at the level of the whole brain (total volume) and in 45 parcels (2.8% of total parcels). Parcels with greater volume included inferior, middle, and superior temporal gyrus, inferior and superior frontal gyrus, precuneus, and fusiform gyrus. Converging evidence from a standard Freesurfer pipeline also identified greater volume in a number of overlapping regions.LimitationsThe method for determining "density" is not a direct measure of neuronal density, and this study cannot reveal underlying cellular differences. While this study represents possibly the largest single-site sample of its kind, it is underpowered to detect very small differences.ConclusionsThese results provide compelling evidence that autism is associated with regional GM volumetric differences, which are more prominent than density differences. This underscores the importance of examining volume and density separately, and suggests that direct measures of volume (e.g. region-of-interest or tensor-based morphometry approaches) may be more sensitive to autism-relevant differences in neuroanatomy than concentration/density-based approaches.
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Project description:Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.
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Project description:Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held-out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr = .012) and lower functioning on the Children's Global Assessment Scale (pcorr = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr = .008). Brain age prediction was more accurate using ComBat-harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.
| S-EPMC8410534 | biostudies-literature
Project description:Sensory processing (i.e., the manner in which the nervous system receives, modulates, integrates, and organizes sensory stimuli) is critical when humans are deciding how to react to environmental demands. Although behavioral studies have shown that there are stable individual differences in sensory processing, the neural substrates that implement such differences remain unknown. To investigate this issue, structural magnetic resonance imaging scans were acquired from 51 healthy adults and individual differences in sensory processing were assessed using the Sensory Profile questionnaire (Brown et al.: Am J Occup Ther 55 (2001) 75-82). There were positive relationships between the Sensory Profile modality-specific subscales and gray matter volumes in the primary or secondary sensory areas for the visual, auditory, touch, and taste/smell modalities. Thus, the present results suggest that individual differences in sensory processing are implemented by the early sensory regions. Hum Brain Mapp 38:6206-6217, 2017. © 2017 Wiley Periodicals, Inc.
| S-EPMC6867006 | biostudies-literature
Project description:BackgroundAutism spectrum disorder (ASD) is a complex neurodevelopmental condition. Understanding the brain's microstructure and its relationship to clinical characteristics is important to advance our understanding of the neural supports underlying ASD. In the current work, we implemented Gray-Matter Based Spatial Statistics (GBSS) to examine and characterize cortical microstructure and assess differences between typically developing (TD) and autistic males.MethodsA multi-shell diffusion MRI (dMRI) protocol was acquired from 83 TD and 70 autistic males (5-to-21-years) and fit to the DTI and NODDI models. GBSS was performed for voxelwise analysis of cortical gray matter (GM). General linear models were used to investigate group differences, while age-by-group interactions assessed age-related differences between groups. Within the ASD group, relationships between cortical microstructure and measures of autistic symptoms were investigated.ResultsAll dMRI measures were significantly associated with age across the GM skeleton. Group differences and age-by-group interactions are reported. Group-wise increases in neurite density in autistic individuals were observed across frontal, temporal, and occipital regions of the right hemisphere. Significant age-by-group interactions of neurite density were observed within the middle frontal gyrus, precentral gyrus, and frontal pole. Negative relationships between neurite dispersion and the ADOS-2 Calibrated Severity Scores (CSS) were observed within the ASD group.DiscussionFindings demonstrate group and age-related differences between groups in neurite density in ASD across right-hemisphere brain regions supporting cognitive processes. Results provide evidence of altered neurodevelopmental processes affecting GM microstructure in autistic males with implications for the role of cortical microstructure in the level of autistic symptoms.ConclusionUsing dMRI and GBSS, our findings provide new insights into group and age-related differences of the GM microstructure in autistic males. Defining where and when these cortical GM differences arise will contribute to our understanding of brain-behavior relationships of ASD and may aid in the development and monitoring of targeted and individualized interventions.
| S-EPMC9817031 | biostudies-literature
Project description:BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental condition commonly studied in the context of early childhood. As ASD is a life-long condition, understanding the characteristics of brain microstructure from adolescence into adulthood and associations to clinical features is critical for improving outcomes across the lifespan. In the current work, we utilized Tract Based Spatial Statistics (TBSS) and Gray Matter Based Spatial Statistics (GBSS) to examine the white matter (WM) and gray matter (GM) microstructure in neurotypical (NT) and autistic males.MethodsMulti-shell diffusion MRI was acquired from 78 autistic and 81 NT males (12-to-46-years) and fit to the DTI and NODDI diffusion models. TBSS and GBSS were performed to analyze WM and GM microstructure, respectively. General linear models were used to investigate group and age-related group differences. Within the ASD group, relationships between WM and GM microstructure and measures of autistic symptoms were investigated.ResultsAll dMRI measures were significantly associated with age across WM and GM. Significant group differences were observed across WM and GM. No significant age-by-group interactions were detected. Within the ASD group, positive relationships with WM microstructure were observed with ADOS-2 Calibrated Severity Scores.ConclusionUsing TBSS and GBSS our findings provide new insights into group differences of WM and GM microstructure in autistic males from adolescence into adulthood. Detection of microstructural differences across the lifespan as well as their relationship to the level of autistic symptoms will deepen to our understanding of brain-behavior relationships of ASD and may aid in the improvement of intervention options for autistic adults.
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