Brain Regions Research Articles

A causal role of the right dorsolateral prefrontal cortex in random exploration

Decision to explore new options with uncertain outcomes or exploit familiar options with known outcomes is a fundamental challenge that the brain faces in almost all real-life decisions. Previous studies have shown that humans use two main explorative strategies to negotiate this explore-exploit tradeoff. Exploring for the sake of information is called directed exploration, and exploration driven by behavioral variability is known as random exploration. While previous neuroimaging studies have shown different neural correlates for these explorative strategies, including right frontopolar cortex (FPC), right dorsolateral prefrontal cortex (DLPFC), and dorsal anterior cingulate cortex (dACC), there is still a lack of causal evidence for most of these brain regions. Here, we focused on the right DLPFC, which was previously supported to be involved in exploration. Using the continuous theta burst stimulation (cTBS) and Horizon task on twenty-five healthy right-handed adult participants, we showed that inhibiting rDLPFC did not change directed exploration but selectively reduced random exploration, by increasing reward sensitivity over the average reward of each option. This suggests a causal role for rDLPFC in random exploration, and further supports dissociable neural implementations for these two explorative strategies.

Multimodal subspace independent vector analysis effectively captures the latent relationships between brain structure and function.

A key challenge in neuroscience is to understand the structural and functional relationships of the brain from high-dimensional, multimodal neuroimaging data. While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the relationships between true latent sources are likely more complex - statistical dependence may exist within and between modalities, and span one or more dimensions. Here we present Multimodal Subspace Independent Vector Analysis (MSIVA), a methodology to capture both joint and unique vector sources from multiple data modalities by defining both cross-modal and unimodal subspaces with variable dimensions. In particular, MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding sub-spaces across modalities. As we demonstrate, a main benefit of MSIVA is the ability to capture subject-level variability at the voxel level within independent subspaces, contrasting with the rigidity of traditional methods that share the same independent components across subjects. We compared MSIVA to a unimodal initialization baseline and a multimodal initialization baseline, and evaluated all three approaches with five candidate subspace structures on both synthetic and neuroimaging datasets. We show that MSIVA successfully identified the ground-truth subspace structures in multiple synthetic datasets, while the multimodal baseline failed to detect high-dimensional subspaces. We then demonstrate that MSIVA better detected the latent subspace structure in two large multimodal neuroimaging datasets including structural MRI (sMRI) and functional MRI (fMRI), compared with the unimodal baseline. From subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis, our findings suggest that the estimated sources from MSIVA with optimal subspace structure are strongly associated with various phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to multiple phenotype measures such as age (e.g., cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (e.g., cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), schizophrenia (e.g., cerebellum, temporal pole, and frontal operculum cortex in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on phenotypic and neuropsychiatric biomarkers of linked brain structure and function.

The brain hierarchically represents the past and future during multistep anticipation

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales.

ReMiND: Recovery of missing neuroimaging using diffusion models with application to Alzheimer’s disease

Abstract Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer’s disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation. The performance of models was compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders. Experimental results show that our method can generate 3D structural MRI with high similarity to ground-truth images at designated visits. Furthermore, images generated using ReMiND show relatively lower differences in volume estimation between the imputed and observed images compared to images generated by forward filling or autoencoders. Additionally, ReMiND provides more accurate estimated rates of atrophy over time in important anatomical brain regions than the two comparator methods. Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.

Network analysis of α-synuclein pathology progression reveals p21-activated kinases as regulators of vulnerability.

Longitudinal α-synuclein pathology assessment in 1046 brain regions over 9 monthsLinear diffusion modeling derivation of network vulnerability to α-synuclein pathologyPANGEA: assessment of over 19,000 genes in 302 brain regionsGroup II PAK inhibitor prevents α-synuclein pathology and neuron death.

Exploring low- and high-order functional connectivity in chronic ankle instability through resting-state fMRI.

The functional connectivity (FC) has emerged as a valuable tool for comprehending the cerebral operational mechanism. Understanding the FC changes in patients with chronic ankle instability (CAI) helps reveal the underlying central nervous system mechanisms of the disease and provides clues for developing personalized treatment plans. To explore differences between low- and high-order FC in patients with CAI and healthy controls, as well as the correlation among the feature connections and clinical data. In our study, we recruited 40 patients with CAI and 42 healthy individuals who had not experienced ankle injuries. All participants underwent clinical assessments of ankle joints, collected the number of ankle sprains within the past 6 months, and performed resting-state functional magnetic resonance imaging (rs-fMRI) scans. Pearson correlation and matrix variate normal distribution (MVND) were used to construct low-order and high-order FC networks, respectively. Feature selections between groups were performed by two-sample t-tests, and a multi-kernel support vector machine (MK-SVM) was subsequently applied to combine the multiple connection patterns for the classification. Using leave-one-out cross-validation (LOOCV) to assess classification performance and identify the consensus connections contributing most to classification. FC was reduced in certain brain regions of CAI patients. More consensus connections were recognized in low-order FC network than in high-order FC network. The highest classification accuracy of 91.30% was achieved by combining three connection patterns. The most discriminating functional connections were primarily centered on the default mode network and spanned the visual network, sensorimotor network, ventral attention network, and central executive network. In addition, FC strength in the left cingulate and paracingulate gyrus (DCG.L) and right superior temporal gyrus (STG.R) was negatively correlated with the number of ankle sprains in the past 6 months in all FC networks (p<0.05). Abnormalities in connectivity in patients with CAI were observed in both low- and high-order FC networks. The adaptive changes in the brain related to CAI may extend beyond the sensorimotor networks, primarily involving higher-level default mode networks associated with attention. Moreover, the FC strength between DCG.L and STG.R may predict the risk of ankle re-sprains and help clinicians develop personalized treatment plans.

Consistent activation differences versus differences in consistent activation: Evaluating meta-analytic contrasts

Abstract Meta-analytic contrasts are a promising aspect of coordinate-based meta-analyses in neuroimaging research as they facilitate the statistical comparison of two meta-analytic results. They have been used for a multitude of comparisons, such as task conditions, cognitive processes, and groups. However, it remains to be tested how the results of meta-analytic contrasts relate to those of classic meta-analyses and vice versa. Here we present a comprehensive empirical investigation of this issue using four datasets from three different cognitive domains: working memory, working memory load, cognitive interference processing, and emotional face processing. For all four datasets, we compared the results of a standard meta-analysis across prototypical contrasts (condition A &amp;gt; condition B) reported in individual experiments with those of a contrast between two individual meta-analyses of the same conditions (meta-analysis condition A &amp;gt; meta-analysis condition B). In the meta-analytic contrasts similar brain regions as in the standard meta-analysis were found but with relatively distinct spatial activation patterns. Additionally, fewer regions were revealed in the meta-analytic contrasts, especially in areas where the conditions spatially overlapped. This can be ascribed to the loss of information on the strength of activations in meta-analytic contrasts, across which standard meta-analysis summarize. In one dataset, additional regions were found in the meta-analytic contrast, potentially due to task effects. Our results demonstrate that meta-analytic contrasts can yield similar results to standard meta-analyses but are sparser. This confirms the overall validity, but also limited ability to capture all regions found in standard meta-analyses. Notable differences observed in some cases indicate that such contrasts cannot be taken as an easy substitute for classic meta-analyses of experiment-level contrasts, warranting further research into the boundary conditions for agreement.

Microscopy assessment of a fluorescence [18F] flortaucipir analog (T726) shows neuropathological overlap with 3R and 4R tau lesions.

[18F] flortaucipir (FTP) binding to paired helical filament (PHF) tau in Alzheimer's disease (AD) is well accepted. Binding to 3R and 4R tau in frontotemporal lobar degeneration (FTLD) is controversial. We aimed to investigate whether an FTP fluorescent analog (T726) can help shed light on this controversy. We assessed T726 binding to amyloid beta (Aβ) and different tau isoforms in nine subjects (one control, three with Alzheimer's disease [AD], and five with FTLD) with different 3R and 4R tauopathies using fluorescence confocal microscopy. T726 did not colocalize with Aβ but showed significant co-localization with PHF tau in AD. We also observed some, albeit limited, co-localization of T726 with 3R and 4R tau lesions in FTLD. This study's findings support FTP binding to some 3R and 4R tau lesions in FTLD. Further studies are needed to understand the biology of why FTP binds some but not all FTLD tau lesions. Flortaucipir analog (T726) showed significant co-localization with paired helical filament (PHF) tau in Alzheimer's disease (AD). Colocalization between T726 with 3R and 4R tau lesions was observed in frontotemporal lobar degeneration (FTLD). Not all 4R tau lesions bind to T726 across different FTLD brain regions.

Beneficial Effects of Ginger Root Extract on Pain Behaviors, Inflammation, and Mitochondrial Function in the Colon and Different Brain Regions of Male and Female Neuropathic Rats: A Gut-Brain Axis Study.

Neuroinflammation and mitochondrial dysfunction have been implicated in the progression of neuropathic pain (NP) but can be mitigated by supplementation with gingerol-enriched ginger (GEG). However, the exact benefits of GEG for each sex in treating neuroinflammation and mitochondrial homeostasis in different brain regions and the colon remain to be determined. Evaluate the effects of GEG on emotional/affective pain and spontaneous pain behaviors, neuroinflammation, as well as mitochondria homeostasis in the amygdala, frontal cortex, hippocampus, and colon of male and female rats in the spinal nerve ligation (SNL) NP model. One hundred rats (fifty males and fifty females) were randomly assigned to five groups: sham + vehicle, SNL + vehicle, and SNL with three different GEG doses (200, 400, and 600 mg/kg BW) for 5 weeks. A rat grimace scale and vocalizations were used to assess spontaneous and emotional/affective pain behaviors, respectively. mRNA gene and protein expression levels for tight junction protein, neuroinflammation, mitochondria homeostasis, and oxidative stress were measured in the amygdala, frontal cortex, hippocampus, and colon using qRT-PCR and Western blot (colon). GEG supplementation mitigated spontaneous pain in both male and female rats with NP while decreasing emotional/affective responses only in male NP rats. GEG supplementation increased intestinal integrity (claudin 3) and suppressed neuroinflammation [glial activation (GFAP, CD11b, IBA1) and inflammation (TNFα, NFκB, IL1β)] in the selected brain regions and colon of male and female NP rats. GEG supplementation improved mitochondrial homeostasis [increased biogenesis (TFAM, PGC1α), increased fission (FIS, DRP1), decreased fusion (MFN2, MFN1) and mitophagy (PINK1), and increased Complex III] in the selected brain regions and colon in both sexes. Some GEG dose-response effects in gene expression were observed in NP rats of both sexes. GEG supplementation decreased emotional/affective pain behaviors of males and females via improving gut integrity, suppressing neuroinflammation, and improving mitochondrial homeostasis in the amygdala, frontal cortex, hippocampus, and colon in both male and female SNL rats in an NP model, implicating the gut-brain axis in NP. Sex differences observed in the vocalizations assay may suggest different mechanisms of evoked NP responses in females.

Mapping the glial transcriptome in Huntington’s disease using snRNAseq: selective disruption of glial signatures across brain regions

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease with a fatal outcome. There is accumulating evidence of a prominent role of glia in the pathology of HD, and we investigated this by conducting single nuclear RNA sequencing (snRNAseq) of human post mortem brain in four differentially affected regions; caudate nucleus, frontal cortex, hippocampus and cerebellum. Across 127,205 nuclei from donors with HD and age/sex matched controls, we found heterogeneity of glia which is altered in HD. We describe prominent changes in the abundance of certain subtypes of astrocytes, microglia, oligodendrocyte precursor cells and oligodendrocytes between HD and control samples, and these differences are widespread across brain regions. Furthermore, we highlight possible mechanisms that characterise the glial contribution to HD pathology including depletion of myelinating oligodendrocytes, an oligodendrocyte-specific upregulation of the calmodulin-dependent 3’,5’-cyclic nucleotide phosphodiesterase 1 A (PDE1A) and an upregulation of molecular chaperones as a cross-glial signature and a potential adaptive response to the accumulation of mutant huntingtin (mHTT). Our results support the hypothesis that glia have an important role in the pathology of HD, and show that all types of glia are affected in the disease.

Computer Aided Based Performance Analysis of Glioblastoma Tumor Detection Methods using UNET-CNN

Brain tumors are the life killing and threatening disease which affects all age groups around the world. The timely detection and followed by the perspective treatments saves the human life. The tumor regions in brain are detected and segmented using UNET-CNN architecture in this paper. During training process of the proposed work, both Glioblastoma and Healthy brain Magnetic Resonance Imaging (MRI) is preprocessed and then multi level transform is applied on the preprocessed image. The features are further computed from the transformed coefficients and these features are trained by UNET-CNN architecture to obtain trained vectors. During testing process of the proposed work, the test brain MRI image is preprocessed and then decomposed coefficients are obtained by multi level transform. Features are computed from these decomposed coefficients and they are classified using UNET-CNN architecture with the trained vectors. The simulation results of the developed methodology are compared with similar studies on both BRATS 2017 and BRATS 2018 datasets

Elucidating a statistical learning brain network: Coordinate-based meta-analyses and functional connectivity profiles of artificial grammar learning in healthy adults

Abstract Language rehabilitation centers on modifying its use through experience-based neuroplasticity. Statistical learning of language is essential to its acquisition and likely its rehabilitation following brain injury, but its corresponding brain networks remain elusive. Coordinate-based meta-analyses were conducted to identify common and distinct brain activity across 25 studies coded for meta-data and experimental contrasts (Grammatical and Ungrammatical). The resultant brain regions served as seeds for profiling functional connectivity in large task-independent and task-dependent data sets. Hierarchical clustering of these profiles grouped brain regions into three subnetworks associated with statistical learning processes. Functional decoding clarified the mental operations associated with those subnetworks. Results support a left-dominant language sub-network and two cognitive control networks as scaffolds for language rule identification, maintenance, and application in healthy adults. These data suggest that cognitive control is necessary to track regularities across stimuli and imperative for rule identification and application of grammar. Future empirical investigation of these brain networks in language learning in individuals with brain injury will clarify their prognostic role in language recovery.

Neurodevelopmental Pathways to Obesity and Type 2 Diabetes: Insights From Prenatal Exposure to Maternal Obesity and Gestational Diabetes Mellitus: A Report on Research Supported by Pathway to Stop Diabetes.

Incidences of childhood obesity and type 2 diabetes (T2D) are climbing at alarming rates. Evidence points to prenatal exposures to maternal obesity and gestational diabetes mellitus (GDM) as key contributors to these upward trends. Children born to mothers with these conditions face higher risks of obesity and T2D, beyond genetic or shared environmental factors. The underpinnings of this maternal-fetal programming are complex. However, animal studies have shown that such prenatal exposures can lead to changes in brain pathways, particularly in the hypothalamus, leading to obesity and T2D later in life. This article highlights significant findings stemming from research funded by my American Diabetes Association Pathway Accelerator Award and is part of a series of Perspectives that report on research funded by the American Diabetes Association Pathway to Stop Diabetes program. This critical support, received more than a decade ago, paved the way for groundbreaking discoveries, translating the neural programming findings from animal models into human studies and exploring new avenues in maternal-fetal programming. Our BrainChild cohort includes >225 children, one-half of whom were exposed in utero to maternal GDM and one-half born to mothers without GDM. Detailed studies in this cohort, including neuroimaging and metabolic profiling, reveal that early fetal exposure to maternal GDM is linked to alterations in brain regions, including the hypothalamus. These neural changes correlate with increased energy intake and predict greater increases in BMI, indicating that early neural changes may underlie and predict later obesity and T2D, as observed in animal models. Ongoing longitudinal studies in this cohort will provide critical insights toward breaking the vicious cycle of maternal-child obesity and T2D.

Recovery after human bone marrow mesenchymal stem cells (hBM-MSCs)-derived extracellular vesicles (EVs) treatment in post-MCAO rats requires repeated handling.

Rehabilitation is the only current intervention that improves sensorimotor function in ischemic stroke patients, similar to task-specific intensive training in animal models of stroke. Bone marrow mesenchymal stem cells (BM-MSCs)-derived extracellular vesicles (EVs) are promising in restoring brain damage and function in stroke models. Additionally, the non-invasive intranasal route allows EVs to reach the brain and target specific ischemic regions. Yet unclear is how handling might enhance recovery or influence other therapies such as EVs after stroke. We used the transient middle cerebral artery occlusion (MCAO) model of stroke in rats to assess how intensive handling alone, in the form of sensorimotor behavioral tests, or in combination with an intranasal treatment of EVs restored neurological function and ischemic damage. Handled rats were exposed to a battery of sensorimotor tests, including the modified Neurological Severity Score (mNSS), beam balance, corner, grid walking, forelimb placement, and cylinder tests, together with Magnetic Resonance Imaging (MRI) at 2, 7, 14, 21, and 28 days post-stroke (dps). Handled MCAO rats were also exposed to an intranasal multidose or single dose of EVs. Non-handled rats were evaluated only by mNSS and MRI at 2, 28, and 56 dps and were treated with a single intranasal dose of EVs. Our results showed that handling animals after MCAO is necessary for EVs to work at the tested dose and frequency, and that a single cumulative dose of EVs further improves the neurological function recovered during handling. These results show the importance of rehabilitation in combination with other treatments such as EVs, and highlight how extensive behavioral testing might influence functional recovery after stroke.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data.

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions by leveraging the power of multivariate analyses of large datasets. While deep learning (DL) offers another promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address these challenges and describe an interpretable predictive pipeline for inferring Autism diagnosis using 3D DL applied to minimally processed structural MRI scans. We trained 3D DL models to predict Autism diagnosis using the openly available ABIDE I and II datasets (n = 1329, split into training, validation, and test sets). Importantly, we did not perform transformation to template space, to reduce bias and maximize sensitivity to structural alterations associated with Autism. Our models attained predictive accuracies equivalent to those of previous machine learning (ML) studies, while side-stepping the time- and resource-demanding requirement to first normalize data to a template. Our interpretation step, which identified brain regions that contributed most to accurate inference, revealed regional Autism-related alterations that were highly consistent with the literature, encompassing a left-lateralized network of regions supporting language processing. We have openly shared our code and models to enable further progress towards remaining challenges, such as the clinical heterogeneity of Autism and site effects, and to enable the extension of our method to other neuropsychiatric conditions.

Methylphenidate differentially alters corticostriatal connectivity after traumatic brain injury.

Traumatic brain injury commonly impairs attention and executive function, and disrupts the large-scale brain networks that support these cognitive functions. Abnormalities of functional connectivity are seen in corticostriatal networks, which are associated with executive dysfunction and damage to neuromodulatory catecholaminergic systems caused by head injury. Methylphenidate, a stimulant medication that increases extracellular dopamine and noradrenaline, can improve cognitive function following TBI. In this experimental medicine add-on study to a randomised, double-blind, placebo-controlled clinical trial, we test whether administration of methylphenidate alters corticostriatal network function and influences drug response. 43 moderate-severe traumatic brain injury patients received 0.3 mg/kg of methylphenidate or placebo twice a day in 2-week blocks. 28 patients were included in the neuropsychological and functional imaging analysis (4 females, mean age 40.9±12.7, range 20-65) and underwent functional MRI and neuropsychological assessment after each block. 123I-Ioflupane SPECT Dopamine Transporter (DAT) scans were performed, and specific binding ratios were extracted from caudate subdivisions. Functional connectivity and the relationship to cognition was compared between drug and placebo conditions. Methylphenidate increased caudate to anterior cingulate cortex functional connectivity compared to placebo and decreased connectivity from the caudate to default mode network. Connectivity within the default mode network was also decreased by methylphenidate administration and there was a significant relationship between caudate functional connectivity and DAT binding during methylphenidate administration. Methylphenidate significantly improved executive function in TBI patients, and this was associated with alterations in the relationship between executive function and right anterior caudate functional connectivity. Functional connectivity is strengthened to brain regions including the anterior cingulate that are activated when attention is focused externally. These results show that methylphenidate alters caudate interactions with cortical brain networks involved in executive control. In contrast, caudate functional connectivity reduces to default mode network regions involved in internally focused attention and that deactivate during tasks that require externally focused attention. These results suggest that the beneficial cognitive effects of methylphenidate may be mediated through its impact on the caudate. Methylphenidate differentially influences how the caudate interacts with large-scale functional brain networks that exhibit co-ordinated but distinct patterns of activity required for attentionally demanding tasks.

Exploring the links among brain iron accumulation, cognitive performance, and dietary intake in older adults: A longitudinal MRI study

This study evaluated longitudinal brain iron accumulation in older adults, its association with cognition, and the role of specific nutrients in mitigating iron accumulation. MRI-based, quantitative susceptibility mapping estimates of brain iron concentration were acquired from seventy-two healthy older adults (47 women, ages 60–86) at a baseline timepoint (TP1) and a follow-up timepoint (TP2) 2.5–3.0 years later. Dietary intake was evaluated at baseline using a validated questionnaire. Cognitive performance was assessed at TP2 using the uniform data set (Version 3) neuropsychological tests of episodic memory (MEM) and executive function (EF). Voxel-wise, linear mixed-effects models, adjusted for longitudinal gray matter volume alterations, age, and several non-dietary lifestyle factors revealed brain iron accumulation in multiple subcortical and cortical brain regions, which was negatively associated with both MEM and EF performance at T2. However, consumption of specific dietary nutrients at TP1 was associated with reduced brain iron accumulation. Our study provides a map of brain regions showing iron accumulation in older adults over a short 2.5-year follow-up and indicates that certain dietary nutrients may slow brain iron accumulation.

Amyloid deposition and its association with depressive symptoms and cognitive functions in late-life depression: a longitudinal study using amyloid-β PET images and neuropsychological measurements

BackgroundAlthough depression is linked to an increased risk of dementia, the association between late-onset depression (LOD) and amyloid burden remains unclear. This study aimed to determine amyloid deposition in patients with LOD compared to healthy controls (HC) using amyloid-beta (Aβ) positron emission tomography (PET) images and neuropsychological assessments.MethodsForty patients first diagnosed with major depressive disorder after the age of 60 (LOD) and twenty-one healthy volunteers (HC) were enrolled. Depression and anxiety were evaluated using the 17-item Hamilton Depression Scale, Hamilton Anxiety Rating Scale, and Clinical Global Impression Scale. Cognitive function was assessed using the Korean versions of the Mini-Mental Status Examination, Montreal Cognitive Assessment, and Seoul Neuropsychological Screening Battery at baseline and 3-month follow-up. 18F-florbetapir PET images were co-registered with T1-weighted magnetic resonance images.ResultsThere was no significant difference in Aβ deposition between LOD and HC groups. No significant correlation between Aβ burden and depressive symptom severity was found in LOD patients. Higher somatic anxiety was correlated with lower Aβ burden in multiple brain regions, including the left inferior frontal lobe (p = 0.009), right anterior cingulate (p = 0.003), and right superior frontal lobe (p = 0.009). Despite cognitive recovery in areas such as attention (Digit Span Forward, p = 0.026), memory (Auditory Verbal Learning Test Recall Total, p = 0.010; Rey Complex Figure Test Delayed Recall, p = 0.039), and frontal executive function (Contrasting Program, p = 0.033) after three months of antidepressant treatment, cognitive improvement showed no association with amyloid deposition.ConclusionsThese findings suggest distinct mechanisms may underlie amyloid deposition in neurodegenerative changes associated with depression. While amyloid burden in specific brain regions negatively correlated with somatic anxiety, it showed no significant correlation with the severity of depression or overall cognitive function.

Identification of brain region-specific landscape and functions of clustered circRNAs in Alzheimer's disease using circMeta2.

Alzheimer's disease (AD) is an age-related neurodegenerative disorder with regulatory RNAs playing significant roles in its etiology. Circular RNAs (CircRNA) are enriched in human brains and contribute to AD progression. Many circRNA isoforms derived from same gene loci share common back splicing sites, thus often form clusters and work as a group to additively regulate their downstream targets. Unfortunately, the coordinated role of clustered circRNAs is often overlooked in individual circRNA differential expression (DE) analysis. To address these challenges, we develop circMeta2, a computational tool designed to perform DE analysis focused on circRNA clusters, equipped with modules tailored for both a small sample of biological replicates and a large-scale population study. Using circMeta2, we identify brain region-specific circRNA clusters from six distinct brain regions in the ENCODE datasets, as well as brain region-specific alteration of circRNA clusters signatures associated with AD from Mount Sinai brain bank (MSBB) AD study. We also illustrate how AD-associated circRNA clusters within and across different brain regions work coordinately to contribute to AD etiology by impacting miRNA-mediated gene expression and identified key circRNA clusters that associated with AD progression and severity. Our study demonstrates circMeta2 as a highly accuracy and robust tool for analyzing circRNA clusters, offering valuable molecular insights into AD pathology.