The iron-dopamine D1 coupling modulates neural signatures of working memory across adult lifespan.

Brain iron overload and decreased integrity of the dopaminergic system have been independently reported as brain substrates of cognitive decline in aging. Dopamine (DA), and iron are co-localized in high concentrations in the striatum and prefrontal cortex (PFC), but follow opposing age-related trajectories across the lifespan. DA contributes to cellular iron homeostasis and the activation of D1-like DA receptors (D1DR) alleviates oxidative stress-induced inflammatory responses, suggesting a mutual interaction between these two fundamental components. Still, a direct in-vivo study testing the iron-D1DR relationship and their interactions on brain function and cognition across the lifespan is rare. Using PET and MRI data from the DyNAMiC study (n=180, age=20-79, %50 female), we showed that elevated iron content was related to lower D1DRs in DLPFC, but not in striatum, suggesting that dopamine-rich regions are less susceptible to elevated iron. Critically, older individuals with elevated iron and lower D1DR exhibited less frontoparietal activations during the most demanding task, which in turn was related to poorer working-memory performance. Together, our findings suggest that the combination of elevated iron load and reduced D1DR contribute to disturbed PFC-related circuits in older age, and thus may be targeted as two modifiable factors for future intervention.

Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks.

Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-ß positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.

An analysis-ready and quality controlled resource for pediatric brain white-matter research.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

White-Matter Integrity and Working Memory: Links to Aging and Dopamine-Related Genes.

Working memory, a core function underlying many higher-level cognitive processes, requires cooperation of multiple brain regions. White matter refers to myelinated axons, which are critical to interregional brain communication. Past studies on the association between white-matter integrity and working memory have yielded mixed findings. Using voxelwise tract-based spatial statistics analysis, we investigated this relationship in a sample of 328 healthy adults from 25 to 80 years of age. Given the important role of dopamine (DA) in working-memory functioning and white matter, we also analyzed the effects of dopamine-related genes on them. There were associations between white-matter integrity and working memory in multiple tracts, indicating that working-memory functioning relies on global connections between different brain areas across the adult life span. Moreover, a mediation analysis suggested that white-matter integrity contributes to age-related differences in working memory. Finally, there was an effect of the COMT Val158Met polymorphism on white-matter integrity, such that Val/Val carriers had lower fractional anisotropy values than any Met carriers in the internal capsule, corona radiata, and posterior thalamic radiation. As this polymorphism has been associated with dopaminergic tone in the prefrontal cortex, this result provides evidence for a link between DA neurotransmission and white matter. Together, the results support a link between white-matter integrity and working memory, and provide evidence for its interplay with age- and DA-related genes.

The Effect of Body Posture on Resting-State Functional Connectivity.

Introduction: An important but under-investigated confound of functional magnetic resonance imaging (fMRI) is body posture. Although it is well established that body position changes cerebral blood flow, the amount of cerebrospinal fluid in the brain, intracranial pressure, and even the firing rate of certain cell types, there is currently no study that directly examines its effect on fMRI measurements. Moreover, fMRI is typically done in a supine position, which often does not correspond to how these processes are performed in everyday settings. Methods: In this study, 20 healthy adults underwent resting-state fMRI under three body positions: supine, right lateral decubitus (RLD), and left lateral decubitus (LLD). We first investigated whether there were differences in overall organization of whole-brain connectivity between conditions using graph theory. Second, we examined whether functional connectivity of two most studied default mode network (DMN) seeds to the rest of the brain was altered as a function of body position. Results: Nonparametric statistical analyses revealed that global network measures differed among conditions, with the supine and LLD showing identical results which differed when compared to the RLD. There was decreased connectivity for DMN seeds in the RLD condition compared to the supine and LLD, but there were no significant differences between the latter two conditions. Discussion: Potential mechanisms underlying these alterations include gravity, changes in physiology, and body anatomy. Our results suggest that, compared to supine and LLD, the RLD position leads to changes in whole-brain and DMN connectivity. Finally, depending on the research question, combining imaging modalities that allow for more naturalistic settings provides a better understanding of certain phenomena. Impact statement Functional connectivity is sensitive to several confounds, including motion, heart rate, and respiration. Body posture is also an important but under-investigated confound. In this study, healthy adults were scanned in three different positions to investigate whether posture results in changes in connectivity. We found that connectivity was identical if participants were facing up or lying on their left, but it was altered when they were lying on their right. Results suggest that posture can lead to connectivity changes and, in some cases, the combined use of functional magnetic resonance imaging with other techniques might provide a better understanding of the phenomenon of interest.

When functional blurring becomes deleterious: Reduced system segregation is associated with less white matter integrity and cognitive decline in aging.

Healthy aging is accompanied by progressive decline in cognitive performance and concomitant changes in brain structure and functional architecture. Age-accompanied alterations in brain function have been characterized on a network level as weaker functional connections within brain networks along with stronger interactions between networks. This phenomenon has been described as age-related differences in functional network segregation. It has been suggested that functional networks related to associative processes are particularly sensitive to age-related deterioration in segregation, possibly related to cognitive decline in aging. However, there have been only a few longitudinal studies with inconclusive results. Here, we used a large longitudinal sample of 284 participants between 25 to 80 years of age at baseline, with cognitive and neuroimaging data collected at up to three time points over a 10-year period. We investigated age-related changes in functional segregation among two large-scale systems comprising associative and sensorimotor-related resting-state networks. We found that functional segregation of associative systems declines in aging with exacerbated deterioration from the late fifties. Changes in associative segregation were positively associated with changes in global cognitive ability, suggesting that decreased segregation has negative consequences for domain-general cognitive functions. Age-related changes in system segregation were partly accounted for by changes in white matter integrity, but white matter integrity only weakly influenced the association between segregation and cognition. Together, these novel findings suggest a cascade where reduced white-matter integrity leads to less distinctive functional systems which in turn contributes to cognitive decline in aging.

Increased functional homotopy of the prefrontal cortex is associated with corpus callosum degeneration and working memory decline.

Functional homotopy reflects the link between spontaneous activity in a voxel and its counterpart in the opposite hemisphere. Alterations in homotopic functional connectivity (FC) are seen in normal aging, with highest and lowest homotopy being present in sensory-motor and higher-order regions, respectively. Homotopic FC relates to underlying structural connections, but its neurobiological underpinnings remain unclear. The genu of the corpus callosum joins symmetrical parts of the prefrontal cortex (PFC) and is susceptible to age-related degeneration, suggesting that PFC homotopic connectivity is linked to changes in white-matter integrity. We investigated homotopic connectivity changes and whether these were associated with white-matter integrity in 338 individuals. In addition, we examined whether PFC homotopic FC was related to changes in the genu over 10 years and working memory over 5 years. There were increases and decreases in functional homotopy, with the former being prevalent in subcortical and frontal regions. Increased PFC homotopic FC was partially driven by structural degeneration and negatively associated with working memory, suggesting that it reflects detrimental age-related changes.

Functional coherence of striatal resting-state networks is modulated by striatal iron content.

Resting-state spontaneous fluctuations have revealed individual differences in the functional architecture of brain networks. Previous research indicates that the striatal network shows alterations in neurological conditions but also in normal aging. However, the neurobiological mechanisms underlying individual differences in striatal resting-state networks (RSNs) have been less explored. One candidate that may account for individual differences in striatal spontaneous activity is the level of local iron accumulation. Excessive iron in the striatum has been linked to a loss of structural integrity and reduced brain activity during task performance in aging. Using independent component analysis in a sample of 42 younger and older adults, we examined whether higher striatal iron content, quantified using relaxometry, underlies individual differences in spontaneous fluctuations of RSNs in general, and of the striatum in particular. Higher striatal iron content was linked to lower spontaneous coherence within both caudate and putamen RSNs regardless of age. No such links were observed for other RSNs. Moreover, the number of connections between the putamen and other RSNs was negatively associated with iron content, suggesting that iron modulated the degree of cross-talk between the striatum and cerebral cortex. Importantly, these associations were primarily driven by the older group. Finally, a positive association was found between coherence in the putamen and motor performance, suggesting that this spontaneous activity is behaviorally meaningful. A follow-up mediation analysis also indicated that functional connectivity may mediate the link between striatal iron and motor performance. Our preliminary findings suggest that striatal iron potentially accounts for individual differences in spontaneous striatal fluctuations, and might be used as a locus of intervention.

Neurocognitive Profiles of Older Adults with Working-Memory Dysfunction.

Individuals differ in how they perceive, remember, and think. There is evidence for the existence of distinct subgroups that differ in cognitive performance within the older population. However, it is less clear how individual differences in cognition in old age are linked to differences in brain-based measures. We used latent-profile analysis on n-back working-memory (WM) performance to identify subgroups in a large sample of older adults (n = 181; age = 64-68 years). Our analysis identified one larger normal subgroup with higher performance (n = 113; 63%), and a second smaller subgroup (n = 55; 31%) with lower performance. The low-performing subgroup showed weaker load-dependent BOLD modulation and lower connectivity within the fronto-parietal network (FPN) as well as between FPN and striatum during n-back, along with lower FPN connectivity at rest. This group also exhibited lower FPN structural integrity, lower frontal dopamine D2 binding potential, inferior performance on offline WM tests, and a trend-level genetic predisposition for lower dopamine-system efficiency. By contrast, this group exhibited relatively intact episodic memory and associated brain measures (i.e., hippocampal volume, structural, and functional connectivity within the default-mode network). Collectively, these data provide converging evidence for the existence of a group of older adults with impaired WM functioning characterized by reduced cortico-striatal coupling and aberrant cortico-cortical integrity within FPN.

Age-Related Differences in Dynamic Interactions Among Default Mode, Frontoparietal Control, and Dorsal Attention Networks during Resting-State and Interference Resolution.

Resting-state fMRI (rs-fMRI) can identify large-scale brain networks, including the default mode (DMN), frontoparietal control (FPN) and dorsal attention (DAN) networks. Interactions among these networks are critical for supporting complex cognitive functions, yet the way in which they are modulated across states is not well understood. Moreover, it remains unclear whether these interactions are similarly affected in aging regardless of cognitive state. In this study, we investigated age-related differences in functional interactions among the DMN, FPN and DAN during rest and the Multi-Source Interference task (MSIT). Networks were identified using independent component analysis (ICA), and functional connectivity was measured during rest and task. We found that the FPN was more coupled with the DMN during rest and with the DAN during the MSIT. The degree of FPN-DMN connectivity was lower in older compared to younger adults, whereas no age-related differences were observed in FPN-DAN connectivity in either state. This suggests that dynamic interactions of the FPN are stable across cognitive states. The DMN and DAN were anti correlated and age-sensitive during the MSIT only, indicating variation in a task-dependent manner. Increased levels of anticorrelation from rest to task also predicted successful interference resolution. Additional analyses revealed that the degree of DMN-DAN anticorrelation during the MSIT was associated to resting cerebral blood flow (CBF) within the DMN. This suggests that reduced DMN neural activity during rest underlies an impaired ability to achieve higher levels of anticorrelation during a task. Taken together, our results suggest that only parts of age-related differences in connectivity are uncovered at rest and thus, should be studied in the functional connectome across multiple states for a more comprehensive picture.